LAB 1 – Biology Lab Skills

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LAB 1 – Biology Lab Skills

AP BiologyLab 01

Objectives:









LAB 1 – Biology Lab Skills

To understand the importance of laboratory safety.

To understand how liquid volumes are measured.

To investigate the accuracy of beakers, graduated cylinders, Erlenmeyer flasks,

volumetric flasks, pipettes, and micropipettes.

To demonstrate repeatable, accurate pipetting techniques.

To learn how to utilize Excel for organizing statistics and creating graphs.

To understand the use of inferential statistics in experiments (includes mean, standard

deviation, standard error, probability, [Chi-square] analysis).

To learn the basics of spectrophotometry and colorimetry.

To learn how to use a spectrophotometer.

Introduction:

Welcome to an advanced biology class. You might not really understand what you are in for.

When you were just a wee freshmen taking Living Environment, you might have thought that

all biology was like that. However, it is not. There is a lot more to biology than what you saw

from that class. The exercises in this ―Lab‖ have the purpose of getting you familiar with some

of the basic skills that you will use throughout the year and then beyond.

Lab Safety: The following safety rules apply to all laboratory areas.

‣ Do not eat or drink in the laboratory, unless so directed by the instructor.

‣ Wear safety glasses or goggles when directed by your instructor, or during exercises in

which glassware and solutions are heated, or during exercises in which dangerous

fumes may be present, creating a possible hazard to eyes or contact lenses.

‣ Clothes should be appropriate for the laboratory setting…

‣ Wear closed-toe shoes at all times in the laboratory.

‣ Assume that all reagents are poisonous and act accordingly.

Read the labels on reagent containers for safety precautions, and understand the

nature of the chemical that you are using.

Stopper or cap all reagent bottles when not in use.

If reagents come into contact with your skin or eyes, wash the area immediately

with water and immediately inform the laboratory instructor.

Do not pipette anything by mouth.

Discard chemicals as instructed; if you have a question, ask your instructor.

Do not pour chemicals back into containers.

Do not taste or ingest any reagents.

‣ Exercise great caution when using heat, especially when heating chemicals.

Do not leave a Bunsen burner or other flame source unattended.

Do not light a Bunsen burner near flammable materials.

Do not move a lit Bunsen burner.

Keep long hair and loose clothing restricted and well away from any flame.

Turn the gas valve off when the Bunsen burner is not in use.

Use proper ventilation and hoods when instructed.

Do not leave stopper in place when heating test tubes.

Handle hot glassware or lab ware with a test tube clamp or tongs.

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AP BiologyLab 01

‣ Do no operate any laboratory equipment until you have been instructed in its use.

‣ Do not perform unauthorized experiments.

‣ Keep your work area neat, clean, and organized.

‣ Read and understand the experiments you will be doing before coming to the

laboratory.

Follow the procedures set forth by the laboratory manual.

‣ Know the location of emergency equipment:

first aid kit

eyewash bottle

fire extinguisher

showers

fire blanket

‣ Report all accidents to your laboratory instructor immediately.

‣ Discard cracked or broken glass only in the broken glass containers.

‣ Report all unsafe conditions to your instructor.

‣ Clean your work area and glassware and wash your hands before leaving the laboratory.

Measurement

Measurement plays a particularly large role in science. In their studies, scientists gather data,

and to do this they use measurements. Scientists measure the concentration of gases in the

atmosphere, the growth of organisms under varying conditions, the rate of biochemical

reactions, the distance of stars from the earth, and an innumerable number of other things. As

measurements form the basis of scientific inquiry, they are deserving of in-depth analysis in lab.

Biology students, as part of their laboratory experience, will be asked to make measurements or

observations during the course of their investigations. These may be either qualitative or

quantitative. A qualitative measurement/observation describes a characteristic and is not

numerical; a quantitative measurement/observation is numerical. Scientific observations

are usually made as quantitative as possible so that they are easier to evaluate. For example, if

you were trying to find the insect with the world's largest wingspan, quantitative observations

are needed. An insect's wingspan may be qualitatively described as huge, but a quantitative

description of an insect‘s wing span as 20 centimeters would be much more useful for this

project.

In a scientific experiment, the investigator examines the effects of variations in the independent

variable on the dependent variable through measurements. For example, let's assume a

biologist is studying the effect of temperature on plant growth. She sets up several different

temperature conditions, and grows groups of plants from seedlings in each condition. When

the experiment ends, she must compare plant growth in the plants from different temperatures.

But how should she do this? Should she just look at the plants and decide which grew the

best? Should she pick up the plants and "feel" which ones have the greatest mass? Of course

not… She would use some sort of quantitative measurement, such as measuring the height

of each plant's stem in centimeters or determining the total plant biomass in grams. Whichever

measurement she chooses, she would need to utilize an instrument to make it.

Take a minute and look around you at the variety of objects surrounding you. There are

probably a few pens or pencils, a notebook or two, and maybe assorted glassware. While you

may be able to easily distinguish the differences between some objects (e.g., your notebook is

longer than your pen), other differences are more difficult to discern. You may not be able to

easily determine, for example, whether your computer keyboard or your course textbook has

greater mass. Even when we can distinguish differences, it is not always easy to determine the

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AP BiologyLab 01

extent of those differences. You may have noted that your computer monitor is heavier than

your notebook, but is it twice as heavy or three times as heavy? Using only our senses, we

cannot be certain about the answer, so we must take measurements.

To take measurements we need instruments. Instruments include simple things like rulers and

graduated cylinders, and complicated electronics like pH sensors and mass spectrometers. All

of these instruments provide us what our senses cannot – a quantified measure of the

properties of an object. As all scientific measurements utilize the metric system, we must first

say a few things about it before proceeding on.

The metric system is universally used in science because it is a decimal system of measurement

and is easy to convert from one unit of related measurement to another (e.g ., volume to

mass). The metric reference units are the meter (m) for length, the liter (l) for volume, and

the gram (g) for mass. Prefixes are used as part of a unit to indicate a portion of or a multiple

of a reference unit. For example, the prefix milli (m) indicates .001 ( 1 / 1000 ). A millimeter (mm) is

0.001 ( 1 / 1000 ) of a meter. A milliliter (ml) is 0.001 ( 1 / 1000 ) of a liter. A milligram (mg) is 0.001

( 1 / 1000 ) of a gram. Note that the same prefixes are used throughout the metric system,

regardless of whether we‘re discussing length, volume or mass.

See Appendix A of your Lab Manual to see many of the metric prefixes that we will be using this

year.

Volumetrics

Typical volumetric containers used in biology labs include beakers, flasks, cylinders, and

pipettes. In most cases, these are made of glass and calibrated at a specific temperature.

However, for very small volume measurements, micropipettes, constructed out of plastic, with

disposable tips, are used.

A beaker is a simple container for liquids, very commonly used in laboratories. Beakers are

generally cylindrical in shape, with a flat bottom. Beakers are available in a wide range of sizes,

from 10 mL up to several liters. They may be made of glass (typically Pyrex ® ) or of plastic.

Beakers may be covered, perhaps by a watch glass, to prevent contamination or loss of the

contents. Beakers are often graduated, or marked on the side with lines indicating the volume

contained. For instance, a 250 mL beaker might be marked with lines to indicate 50, 100, 150,

200, and 250 mL volumes. The accuracy of these marks can vary from one beaker to another.

A beaker is distinguished from a flask by having sides which are vertical rather than sloping. In

the lab, beakers are used more often than flasks.

A graduated cylinder is used to accurately measure out volumes of liquid chemicals. They are

more accurate and precise for this purpose than beakers or flasks. Often, the largest graduated

cylinders are made of polyethylene or other rigid plastic, making them lighter and less fragile

than glass.

Laboratory flasks come in a number of shapes and a wide range of sizes, but a common

distinguishing aspect is a wider vessel "body" and one (or sometimes more) narrower tubular

sections at the top called necks which have an opening at the top. Like beakers, laboratory

flask sizes are specified by the volume they can hold. Laboratory flasks have traditionally been

made of glass, but can also be made of plastic. Volumetric flasks come with a stopper or cap

for capping the opening at the top of the neck. Stoppers can be made of glass, plastic, or

rubber. In general, flasks can be used for making, holding, containing, collecting, or

volumetrically measuring solutions.

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AP BiologyLab 01

A pipette is a laboratory instrument used to transport an accurately measured volume of liquid.

Pipettes are commonly used in biology. Pipettes come in several designs for various purposes

with differing levels of accuracy and precision, from single piece flexible plastic transfer pipettes

to more complex adjustable or electronic pipettes. A pipette works by creating a vacuum above

the liquid-holding chamber and selectively altering this vacuum to draw up and dispense liquid.

Pipettes that dispense between 1 and 1000 L (1 mL) are termed micropipettes, while

macropipettes (or regular pipetets) dispense greater volumes of liquid.

Note, that volumetric pipettes are designed in such a way that after a fluid is dispensed, a small

drop of liquid will remain in the tip. In general you should not blow this drop out. The correct

volume will be dispensed from the pipette if the side of the tip is touched to the inside wall of

the flask (or beaker).

Some types of the volumetric glass can be used only to measure predefined volume of solution.

Volumetric flasks are designed to contain (TC, sometimes marked as IN) known volume of the

solution, while pipettes are generally designed to deliver (TD, sometimes marked as EX)

known volume (although in some rare cases they can be designed to contain). This is an

important distinction – when you empty a pipette to deliver exactly required volume and you

don‘t have to worry about the solution that is left on the pipette walls and in pipette tip. At the

same time you will never know how much solution was in the pipette. On the contrary,

volumetric flask is known to contain required volume, but if you will pour part the solution to

some other flask you will never know how much of the solution was transferred.

Both kinds of glass were designed this way as they serve different purposes. Volumetric flask is

used to dilute original sample to known volume, so it is paramount that it contains exact

volume. Pipette is used to transfer the solution, so it is important that it delivers known

volume.

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AP BiologyLab 01

Procedure 1:

The accuracy of 100 mL beakers

1. Using a 400 gram top-loading balance, tare a dry 250 mL beaker.

2. Using a 100 mL beaker, as carefully as possible, measure 20 mL of de-ionized (DI)

water.

3. Pour this measured amount of water into the 250 mL beaker. Flick any remaining water

in the 100 ml beaker into the 250 mL beaker.

4. Record the total mass of the water (from this step) to the nearest 0.01 g in Table 1.

5. Again, using the 100 mL beaker, measure 20 mL of water and add it to the 250 mL

beaker.

6. Record the new mass in Table 1.

7. Repeat this procedure until you have 100 mL of water in the beaker on the balance.

8. Calculate the error columns in Table 1.

Table 1: Mass of water volumes measured using a 100 mL beaker.

Total volume of

water (mL)

Total calculated

mass of water

(g)

Total measured

mass of water

(g)

Calculated mass

of water added

in this step (g)

Measured mass

of water added

in this step (g)

Error

Percent Error

20.0 20.00

40.0 40.00

60.0 60.00

80.0 80.00

100.0 100.00

Mean Values:

Procedure 2:

The accuracy of 100 mL graduated cylinders

1. Using a 400 gram top-loading balance, tare a dry 250 mL beaker.

2. Using a 100 mL graduated cylinder, as carefully as possible, measure 20 mL of deionized

(DI) water.

3. Pour this measured amount of water into the 250 mL beaker.

4. Record the mass of the water to the nearest 0.01 g in Table 2.

5. Tare the balance.

6. Again, using the 100 mL graduated cylinder, measure 20 mL of water and add it to the

250 mL beaker.

7. Record the new mass in Table 2.

8. Repeat this procedure until you have 100 mL of water in the beaker on the balance.

9. Calculate the error columns in Table 2.

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AP BiologyLab 01

Table 2: Mass of water volumes measured using a 100 mL graduated cylinder.

Total volume of

water (mL)

Total calculated

mass of water

(g)

Total measured

mass of water

(g)

Calculated mass

of water added

in this step (g)

Measured mass

of water added

in this step (g)

Error

Percent Error

20.0 20.0

40.0 40.0

60.0 60.0

80.0 80.0

100.0 100.0

Mean Values:

Procedure 3:

The accuracy of 50 mL graduated cylinders

1. Using a 400 gram top-loading balance, tare a dry 250 mL beaker.

2. Using a 50 mL graduated cylinder, as carefully as possible, measure 20 mL of de-ionized

(DI) water.

3. Pour this measured amount of water into the 250 mL beaker.

4. Record the mass of the water to the nearest 0.01 g in Table 3.

5. Tare the balance.

6. Again, using the 50 mL graduated cylinder, measure 20 mL of water and add it to the

250 mL beaker.

7. Record the new mass in Table 3.

8. Repeat this procedure until you have 100 mL of water in the beaker on the balance.

9. Calculate the error columns in Table 3.

Table 3: Mass of water volumes measured using a 50 mL graduated cylinder.

Total volume of

water (mL)

Total calculated

mass of water

(g)

Total measured

mass of water

(g)

Calculated mass

of water added

in this step (g)

Measured mass

of water added

in this step (g)

Error

Percent Error

20.0 20.0

40.0 40.0

60.0 60.0

80.0 80.0

100.0 100.0

Mean Values:

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AP BiologyLab 01

Procedure 4:

The accuracy of 25 mL graduated cylinders

1. Using a 400 gram top-loading balance, tare a dry 250 mL beaker.

2. Using a 25 mL graduated cylinder, as carefully as possible, measure 20 mL of de-ionized

(DI) water.

3. Pour this measured amount of water into the 250 mL beaker.

4. Record the mass of the water to the nearest 0.01 g in Table 4.

5. Tare the balance.

6. Again, using the 25 mL graduated cylinder, measure 20 mL of water and add it to the

250 mL beaker.

7. Record the new mass in Table 4.

8. Repeat this procedure until you have 100 mL of water in the beaker on the balance.

9. Calculate the error columns in Table 4.

Table 4: Mass of water volumes measured using a 25 mL graduated cylinder.

Total volume of

water (mL)

Total calculated

mass of water

(g)

Total measured

mass of water

(g)

Calculated mass

of water added

in this step (g)

Measured mass

of water added

in this step (g)

Error

Percent Error

20.0 20.0

40.0 40.0

60.0 60.0

80.0 80.0

100.0 100.0

Mean Values:

Procedure 5:

The accuracy of 25 mL Erlenmeyer flask

1. Using a 400 gram top-loading balance, tare a dry 250 mL beaker.

2. Using a 25 mL Erlenmeyer flask, as carefully as possible, measure 20 mL of de-ionized

(DI) water.

3. Pour this measured amount of water into the 250 mL beaker.

4. Record the mass of the water to the nearest 0.01 g in Table 5.

5. Tare the balance.

6. Again, using the 25 mL Erlenmeyer flask, measure 20 mL of water and add it to the 250

mL beaker.

7. Record the new mass in Table 5.

8. Repeat this procedure until you have 100 mL of water in the beaker on the balance.

9. Calculate the error columns in Table 5.

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AP BiologyLab 01

Table 5: Mass of water volumes measured using a 25 mL Erlenmeyer flask.

Total volume of

water (mL)

Total calculated

mass of water

(g)

Total measured

mass of water

(g)

Calculated mass

of water added

in this step (g)

Measured mass

of water added

in this step (g)

Error

Percent Error

20.0 20.0

40.0 40.0

60.0 60.0

80.0 80.0

100.0 100.0

Mean Values:

Procedure 6:

The accuracy of 50 mL volumetric flask

1. Using a 400 gram top-loading balance, tare a dry 250 mL beaker.

2. Using a 50 mL volumetric flask, as carefully as possible, measure 50 mL of de-ionized

(DI) water.

3. Pour this measured amount of water into the 250 mL beaker.

4. Record the mass of the water to the nearest 0.01 g in Table 6.

5. Pour the contents in the 250 mL beaker down the sink, return to the top-loading

balance, and tare.

6. Again, using the 50 mL volumetric flask, measure 50 mL of water and add it to the 250

mL beaker.

7. Record the mass in Table 6.

8. Repeat steps 5 – 7 for a total of 5 measurements.

9. Calculate the error columns in Table 6.

Table 6: Mass of water volumes measured using a 50 mL volumetric flask.

Volume of

water (mL)

Calculated mass

of water added

in this step (g)

Measured mass

of water added

in this step (g)

Error

Percent Error

50.0

50.0

50.0

50.0

50.0

Mean

Values:

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AP BiologyLab 01

Micropipeting and Microquantity Measurement

This part of the lab introduces sterile pipeting and micropipeting techniques used often in

molecular and microbiology protocols. Mastery of these techniques will be important for good

results in these applications. Most microchemical protocols involve very small volumes of DNA

and other reagents. These require you to use an adjustable micropipette that measures as little

as one microliter (L) a millionth of a liter, compared to millileters (mL) which are only one

thousandth of a liter.

Using a Glass Pipette

Take a 10 mL glass (or nalgene) pipette. Carefully place

the attachment of the three-way bulb (Figure 1) over the

mouth of the pipette. Squeeze the air valve (A) and the

bulb simultaneously to empty the bulb of air. Place the tip

of the pipette below the solution's surface in the beaker.

Gradually squeeze the suction valve (S) to draw liquid into

the pipette. When the liquid is above the specified volume,

stop squeezing the suction valve (S). Do not remove the

bulb from the pipette.




DO NOT ALLOW LIQUID TO ENTER THE PIPETTE BULB. If

the level of the solution is not high enough, squeeze the air

valve (A) and the bulb again to expel the air from the bulb. Draw up more liquid by

squeezing the suction valve (S). If the level of the solution is above the specified volume

(or 0.00 mL in a TD pipette), gently squeeze the empty valve (E) so the meniscus is at the

correct mark.

Touch the tip of the pipette to the inside of the beaker to remove the drop hanging from

the tip. If this drop is not eliminated, the volume transferred will be slightly higher than the

volume desired.

To transfer the solution into the desired vessel, press the empty valve (E) until the meniscus

is at the mark corresponding to the appropriate volume. Touch the tip of the pipette to the

wall of the receiving vessel to remove any liquid from the outside of the tip. Record the final

volume in the pipette. The volume transferred is equal to the final pipette reading minus the

initial pipette reading.

Procedure 7:

The accuracy of glass pipette measurements

1. Using a 400 gram top-loading balance, tare a dry 250 mL beaker.

2. Using a 10 mL glass pipette and pipette bulb, as carefully as possible, measure 10 mL of

de-ionized (DI) water.

3. Dispense this measured amount of water into the 250 mL beaker.

4. Record the mass of the water to the nearest 0.01 g in Table 7.

5. Tare the balance.

6. Again, using the 10 mL glass pipette, measure 10 mL of water and add it to the 250 mL

beaker.

7. Record the mass in Table 7.

8. Repeat this procedure until you have 50 mL of water in the beaker on the balance.

9. Calculate the error columns in Table 7.

Figure 1: Diagram of a pipette bulb.

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AP BiologyLab 01

Table 7: Mass of water volumes measured using a 10 mL glass pipette.

Volume of

water (mL)

Calculated mass

of water added

in this step (g)

Measured mass

of water added

in this step (g)

Error

Percent Error

10.0

10.0

10.0

10.0

10.0

Mean

Values:

Using a Micropipette

THE SOLUTIONS FOR THIS PART OF THE LAB ARE COLORED WATER. THE SAFETY

PRECAUTIONS LISTED BELOW ARE TO PREVENT DAMAGE TO THE MICROPIPETTE.

NEVER SET THE MICROPIPETTE TO A VOLUME BEYOND ITS RANGE.

NEVER ATTEMPT TO USE THE PIPETTE WITHOUT A TIP IN PLACE.

NEVER LAY DOWN A PIPETTE THAT HAS A FILLED TIP.

NEVER LET THE PLUNGER SNAP BACK AFTER WITHDRAWING OR EJECTING FLUID.

1. Take a large-range micropipette (100 L—1000 L). Rotate the control button to the

minimum (100) and maximum (1000) values – do not exceed these values! Notice the

change in the plunger length as the volume is changed.

2. Push the micropipette end firmly in the proper size tip.

3. While withdrawing or expelling fluid, always hold the vessel at nearly eye-level. It is

important that you watch while you pipette.

4. Hold the pipette in a vertical position when filling.

5. To draw fluid, depress the button to the first stop, and hold in this position. Then, dip the

tip into the solution to be pipetted, and draw the fluid into the tip by gradually releasing the

plunger.

6. Slide the tip out along the inside wall of the reagent tube to dislodge any excess fluid

adhering to the outside of the tip.

7. To withdraw the sample, touch the pipette tip to the inside wall of the reaction tube into

which you wish to empty the sample. This creates a capillary effect which helps draw fluid

out of the tip.

8. Slowly depress the button to the first stop. Then press on to the second stop to blow out

the last bit of fluid. Hold the button down in the second position.

9. Slide the pipette out of the reagent tube with the button depressed to the second stop to

avoid sucking any liquid back into the tip.

10. To eject the tip, depress the separate thumb button to ‗launch‘ tip into a waste jar.

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AP BiologyLab 01

11. To prevent contamination of your reagents:

Always add appropriate amounts of a single reagent sequentially to all reaction tubes.

Release each reagent drop onto a new location on the inside wall of the reaction tube.

In this way you can use the same tip to pipette reagent into each reaction tube.

Use a fresh tip for each new reagent you pipette.

12. Label two (2) 1.5 mL tubes and label them A and B.

13. Using the matrix designed for your micropipette, fill each tube to the desire volume. When

using a matrix like the one below, to conserve tips, use the same tip for all aliquots of one

solution, then discard the tip and go to the next solution. Also be sure to touch a different

side of a microtube when you change solutions, so you do not contaminate the tip.

Tube Solution I Solution II Solution III Solution IV

A 100 L 200 L 150 L 550 L

B 150 L 250 L 350 L 250 L

14. Close the tops, and place the reaction tubes in a balanced configuration (see Figure 2 for

examples) in the microfuge rotor. Spinning tubes in an unbalanced position will damage the

microfuge rotor.

Figure 2: Balanced rotor configurations.

15. Spin tubes for a 1-2 second pulse in the microfuge. This will mix and pool reactants into a

droplet in the bottom of each tube.

16. You added a total of 1000 L (1 mL) of reactants into each test tube. Now, set your pipette

to 1000 L (1 mL), and very carefully withdraw the solution from each tube – there should

be no excess fluid in the tube nor any air bubbles in the pipette tip. Discard into the waste

beaker.

17. Obtain a mid-range (10 L—100 L) micropipette.

18. Label two (2) 1.5 mL tubes and label them C and D.

19. Using the matrix designed for your micropipette, fill each tube to the desire volume.

Tube Solution I Solution II Solution III Solution IV

C 15 L 25 L 32 L 28 L

D 11 L 44 L 18 L 27 L

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AP BiologyLab 01

20. Close the tops, and place the reaction tubes in a balanced configuration in the microfuge

rotor. Spinning tubes in an unbalanced position will damage the microfuge rotor.

21. Spin tubes for a 1-2 second pulse in the microfuge. This will mix and pool reactants into a

droplet in the bottom of each tube.

22. You added a total of 100 L of reactants into each test tube. Now, set your pipette to 100

L, and very carefully withdraw the solution from each tube – there should be no excess

fluid in the tube nor any air bubbles in the pipette tip. Discard into the waste beaker.

23. Obtain a small-range (0.5 L —10 L) micropipette.

24. Label three (3), 1.5 mL reaction tubes and label them E, F, and G.

25. Use the matrix below to add each solution sequentially to each of the three (3) tubes. Be

sure to use a fresh pipette tip for each change in solution.

Tube Solution I Solution II Solution III Solution IV

E 4 L 5 L 1 L ----

F 4 L 5 L ---- 1 L

G 4 L 4 L 1 L 1 L

26. Close the tops, and place the reaction tubes in a balanced configuration in the microfuge

rotor. Spinning tubes in an unbalanced position will damage the microfuge rotor. See the

next page for balanced rotor positions. If you have an odd number of tubes, you can put in

blanks that will balance out the arrangement.

27. Spin tubes for a 1-2 second pulse in the microfuge. This will mix and pool reactants into a

droplet in the bottom of each tube.

28. You added a total of 10 L of reactants into each test tube. Now, set your pipette to 100

L, and very carefully withdraw the solution from each tube – there should be no excess

fluid in the tube nor any air bubbles in the pipette tip. Discard into the waste beaker.

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AP BiologyLab 01

Micropipetting Questions


Using the diagram below, draw balanced rotor configurations for 5, 7, 8, 9, and 10 tubes.

HINT: You must use at least that number of tubes…


Which of the following shows an unbalanced rotor? (put an X through the diagram)





The small range digital micropipette measures volumes between 0.5 L and 10.0 L. If you

wish to dispense seven and five-tenths microliters of a fluid with the instrument, what

sequence of numerals would you see on the digital dial?

a. 75/00

b. 75/10

c. 00/75

d. 07/50

A student presses the button on the micropipette to the first position, places it in a liquid

and slowly releases the button. What will most likely occur?

a. ejection of the tip

b. fluid will be drawn up into the tip

c. the last drop of fluid will be pushed our of the tip

d. most, but not all fluid will be expelled from the tip

A student presses the button on the micropipette to the second position. What will most

likely occur?

a. ejection of the tip

b. most of the fluid will be removed from the tip

c. fluid will be drawn up into tip

d. fluid will be ejected and then redrawn into the tip

On a large-range Eppendorf digital micropipette, what volume of liquid is indicated by these

numbers 0 5 0 0

a. 5 L

b. 50 L

c. 500 L

d. 5000 L

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Complete the following conversions:

a. 0.167 mL to L _________

b. 0.05 mL to L _________

c. 42 L to mL _________

d. 182 L to mL _________

e. 0.9 L to mL _________

Identify four (4) important precautions in micropipette use:

_____________________________________________________________________________

_____________________________________________________________________________

_____________________________________________________________________________

_____________________________________________________________________________

Procedure 8:

The accuracy of micropipette measurements

1. Using a 400 gram top-loading balance, tare a dry 250 mL beaker.

2. Using a large-range (100 L—1000 L) micropipette with a fresh tip, measure 1000 L

(1 mL) of de-ionized (DI) water.

3. Dispense this measured amount of water into the 250 mL beaker.

4. Record the mass of the water to the nearest 0.01 g in Table 8.

5. Tare the balance.

6. Again, using the large range micropipette, measure 10 mL of water and add it to the

250 mL beaker.

7. Record the mass in Table 8.

8. Repeat this procedure until you have 5 mL of water in the beaker on the balance.

9. Calculate the error columns in Table 8.

Table 8: Mass of water volumes measured using a large range (100 L—1000 L) micropipette.

Volume of

water (mL)

Calculated mass

of water added

in this step (g)

Measured mass

of water added

in this step (g)

Error

Percent Error

1.000

1.000

1.000

1.000

1.000

Mean

Values:

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AP BiologyLab 01


In procedures 1 through 8, you measured and massed the volume of water. Write a

paragraph in which you answer these questions: What effect, if any, does temperature

have upon these measurements? Are there any markings on your glassware related to this

potential problem? Are there any other interesting markings on your glassware?

_____________________________________________________________________________

_____________________________________________________________________________

_____________________________________________________________________________

_____________________________________________________________________________

_____________________________________________________________________________

_____________________________________________________________________________

_____________________________________________________________________________

Accuracy and Precision

What do these terms mean? Many might think that they are the same. However, there is an

important distinction between the two. Accuracy is how close the measured values are to the

actual values. Precision is how reproducible or repeatable the measured values are.

Measurements can be either accurate, precise, neither, or both. The classic distinction between

the two involves shooting arrows at a target. How accurate the archer is can be seen by

looking at how close the arrows are to the bulls-eye. The closer to the bulls-eye, the more

accurate, the further from the bulls-eye, the less accurate. How precise the archer is can be

shown by how close the cluster of arrows are to each other. The less spread out the arrows

are, the more precise the archer. The more spread out, the less precise.

Look at each target below and decide whether the situation is accurate and/or precise.

Accurate?: Yes / No

Precise?:

Yes / No

Accurate?: Yes / No

Precise?:

Yes / No

Accurate?: Yes / No

Precise?:

Yes / No

Page 15 of 39


AP BiologyLab 01

In Procedures 1-8, you should have calculated the percent error for each volumetric

instrument. The mean of the percent error can be related to the accuracy of each volumetric

instrument. Therefore, the lesser the mean is of the percent error, the more accurate the

measuring device. Inversely, the greater the mean is of the percent error, the more accurate

the measuring device.

However, we cannot determine precision from the above. To determine precision, we must

look at the variance that exists in the set. We can get this by looking at the standard deviation.

The formulas for mean, standard deviation (SD), and standard error of the mean (SEM) are

listed below and are also supplied in Appendix A (Lab 00 – Equations).

SD

The standard deviation (SD) describes the variability between individuals in a sample; the

standard error of the mean (SEM) describes the uncertainty of how the sample mean

represents the overall population mean.

If normally distributed (a bell curve – more on that later), the study sample can be described

entirely by two parameters: the mean and the standard deviation. The SD represents the

variability within the sample; the larger the SD, the higher the variability within the sample.

Although the SD and the SEM are related (see formulas), they give two very different types of

information. Whereas the SD estimates the variability in the study sample, the SEM estimates

the precision and uncertainty of how the study sample represents the underlying population. In

other words, the SD tells us the distribution of individual data points around the mean, and

the SEM informs us how precise our estimate of the mean is.

In Table 9 below, for each volumetric device, fill in your calculated mean, the standard

deviation (SD), and the standard error of the mean (SEM) for each set of percent error values.

Table 9: Mean, standard deviation, and standard error of the volumetric devices measuring a known

quantity of water.

Volumetric device

Mean percent

error

Standard

deviation

Standard Error

of the mean

100 mL beaker

100 mL graduated cylinder

50 mL graduated cylinder

25 mL graduated cylinder

25 mL Erlenmeyer flask

50 mL volumetric flask

10 mL glass pipette

100 L – 1000 L micropipette

Page 16 of 39


AP BiologyLab 01

Using Excel

It is important to not only be able to correctly collect data, but also present it visually – often in

chart and graph form. Data tables and visual representations of data are integral parts of the

results section of the lab write-ups and mini-posters that you will be producing this year. For

general graphing help, refer to Appendix B (Lab 00 – Making Graphs) in your Lab Manual. For

help with Excel, refer to the summer assignment that was given. If you still need help, make

sure you get it – from the teacher or other students in the class. You will have to know how to

do this, so make sure you understand the steps to construct a useful graph.




Using Excel, create a series of 5 graphs (use scatter plot with trendlines) that show the

relationship of total calculated mass versus total measured mass of the water samples

measured for each volumetric device in Procedures 1 through 5. For each graph, also

include a line that shows a direct correlation – you will need to plot a line that is ‗perfect‘ –

(0,0), (20,20), (40,40), (60,60), (80,80), (100,100). Note: Make sure that you label all

axes, complete a reasonable figure caption, have a key/legend of points/lines, etc. These

graphs (and linked data tables used to construct it) will also be collected.

Also using Excel, put all 6 of these lines on one graph (these include the 5 from procedures

1 -5 and the ‗perfect‘ line) on one grid. Do your best make this multi-line graph easy to

interpret – you will need a properly formatted graph with all the bells and whistles (legend,

caption, etc.)

Compare the lines on the above graph with the mean percent error that you calculated for

each volumetric device. Which would you say is the most accurate based on both your

calculations and on the graphical representation of the data? HINT: compare each

trendline for the calculated data with the ‗perfectly correlated [R 2 = 1.000] line.

_____________________________________________________________________________

_____________________________________________________________________________

_____________________________________________________________________________

_____________________________________________________________________________

_____________________________________________________________________________

_____________________________________________________________________________

Page 17 of 39


AP BiologyLab 01

Again, using Excel, create a bar graph with error bars ( standard error) showing the

relationship between type of measuring device and the percentage error. Note: All bars

should be positive in this graph. You can find the Error Bars button in Excel 2007 (and I

believe it is the same for Office 2010) when you click on the graph and then look under

Chart Tools > Layout > Analysis.


Compare the bars on the above graph with the mean percent error including the error bars

for each volumetric device. Which would you say is the most precise based on both your

calculations and on the graphical representation of the data? HINT: For the graph, look at

the size of the error bars.

_____________________________________________________________________________

_____________________________________________________________________________

_____________________________________________________________________________

_____________________________________________________________________________

_____________________________________________________________________________

_____________________________________________________________________________

Inferential Statistics: Introduction

Up to this point, we've discussed the proper methods for taking measurements, and you've

gained some experience with simple instruments. In this section, we will follow up on that by

introducing a new group of statistics – inferential statistics. Before I define inferential statistics,

let me show you why they are useful. Imagine a research study that sought to determine the

effects of temperature on plant biomass. Upon completion of an experiment, the experimenters

would have sets of biomass measurements for each group. What do they do then? They take

the mean of the values for each group and then make conclusions based on this statistic alone?

What if the mean biomass for one group is only slightly higher than that for another group – is

the difference sufficient for her to make a solid conclusion? Inferential statistics allow you to

make comparisons in scientific studies and determine with confidence if differences in treatment

groups truly exist.

Inferential statistics are used to make comparisons between data sets and infer whether the

two data sets are significantly different from one another. It is important to realize that when

dealing with statistics and probability, chance always plays a role. When we compare means

from two groups in an experiment, we are attempting to determine if the two means truly differ

from one another, or if the difference in the means of the groups is simply due to random

chance. The best way to explain this concept is with an example…

Page 18 of 39


AP BiologyLab 01

Chance and ―Significant‖ Differences: A Case Study

After losing a close game in overtime, a local high school football coach accuses the officials of

using a "loaded" coin during the pre-overtime coin toss. He claims that the coin was altered to

come up heads when flipped, his opponents knew this, won the coin toss, and consequently

won the game on their first possession in overtime. He wants the local high school athletic

association to investigate the matter. You are assigned the task of determining if the coach's

accusation stands up to scrutiny. Well, you know that a "fair" coin should land on heads 50%

of the time, and on tails 50% of the time. So how can you test if the coin in question is

doctored? If you flip it ten times and it comes up heads six times, does that validate the

accusation? What if it comes up heads seven times? What about eight times? Does coming up

heads five time prove that it ISN‘T a rigged coin? To make a conclusion, you need to know the

probability of these occurrences.

To examine the potential outcomes of coin flipping, we will use a Binomial Distribution. This

distribution describes the probabilities for events when you have two possible outcomes (heads

or tails) and independent trials (one flip of the coin does not influence the next flip). The

distribution for ten flips of a fair coin is shown in Figure 3.

Figure 3: Binomial Distribution for fair coin with ten flips.

Note that ratio of 5 heads : 5 tails is the most probable, and the probabilities of other

combinations decline as you approach greater numbers of heads or tails. The figure

demonstrates two important points. One, it shows that the expected outcome is the most

probable – in this case a 5 : 5 ratio of heads to tails. Two, it shows that unlikely events can

happen due solely to random chance (e.g., getting 0 heads and 10 tails), but that they have a

very low probability of occurring.

Page 19 of 39


AP BiologyLab 01

Also note that the binomial distribution is rather "jagged" when only ten coin flips are

performed. As the number of trials (coin flips) increases, the shape of the distribution begins to

smooth out and resemble a normal curve. Note how the shape of the curve with 50 trials is

much smoother than the curve for 10 trials, and more representative of a normal curve as seen

in Figure 4. These normal curves are often referred to as a ―bell curve‖.

Figure 4: Binomial distribution for fair coin with 50 flips.

Inferential Statistics: Probability

Normal curves are useful because they allow us to make statistical conclusions about the

likelihood of being a certain distance from the center (mean) of the distribution. In a normal

distribution, there are probabilities associated with differing distances from the mean. Recall

from Algebra 2/Trigonometry that 68% of the values in a sample showing normal distribution

are within one standard deviation of the mean, 95% of values are within two standard

deviations of the mean, and 99% of the values are within three standard deviations of the

mean – Figure 5.

Page 20 of 39


AP BiologyLab 01

Figure 5: Percent of normally distributed values in each interval based on standard deviation.

The difficulty with working with probabilities is knowing when to conclude that an occurrence is

NOT due to random chance. Values far from the mean in a distribution can occur, but will

occur with low probability (Figure 5). We are therefore essentially testing the hypothesis that

the observed data fit a particular distribution. In the coin flip example, we're testing to see if

our results fit those expected from the distribution of a fair coin. So we need to come up with a

point at which we can conclude our results are definitely not part of the distribution we are

testing. So when do you determine that a given data set no longer fits a distribution when

random chance will always play a role? Well, you've got to make an arbitrary decision, and

biologists/statisticians set precedent long ago. Given that 95% of the values in a distribution

fall within two standard deviations of the mean, statisticians have decided that if a result falls

outside of this range, you can determine that your data does not fit the distribution you are

testing. This essentially says that if your result has equal to or less than a 5% chance of

belonging to a particular distribution, then you can conclude with 95% confidence/certainty

(meaning outside the 2 standard deviation interval) that it is not a part of that distribution. As

probabilities are listed as proportions, this means that a result is "statistically significant" if

its occurrence (p-value) is equal to or less than 0.05. This leads to our statistical "rule of

thumb" - whenever a statistical test returns a probability value (or "p-value") equal to or less

than 0.05, we reject the hypothesis that our results fit the distribution we expect to get. The

standard practice in such comparisons is to use a null hypothesis (written as "H 0 "), which states

that the data are not statistically significant and do fit the expected distribution along with an

alternate hypothesis (written as "H A "), which states that the data are statistically significant and

do not fit the expected distribution.

H 0 : The data fit the assigned distribution with 95% confidence and is not statistically

significant.

H a : The data do not fit the assigned distribution with 95% confidence and is statistically

significant.

Page 21 of 39


AP BiologyLab 01

To practice your interpretation of p-values, decide if each of the p-values below indicates that

you should reject your null hypothesis by circling the correct answer.

A. p-value = 0.11 Accept or Reject H 0 ?

B. p-value = 0.56 Accept or Reject H 0 ?

C. p-value = 0.01 Accept or Reject H 0 ?

D. p-value = 0.9 > 0.7 Accept or Reject H 0 ?

E. p-value < 0.005 Accept or Reject H 0 ?

So back to our coin test… It is comparing our result to the expected distribution of a fair coin.

To test the coin, you opt to flip it 50 times, tally the number of heads and tails, and compare

your results to the fair coin distribution. Our null hypothesis would be as follows:

H 0 : The coin is balanced properly and we would expect an even number of heads and

tails when flipped repeatedly with 95% confidence.

H a : The coin is not balanced properly and we would not expect an even number of

heads and tails when flipped repeatedly with 95% confidence.

You obtain the results listed below.

Table 10: Number of times observed when a coin was flipped 50 times.

Heads

Tails

33 17

So what does this mean? Referencing the distribution (Figure 4 above), we see that a ratio of

33 heads to 17 tails would only occur about 1% of the time if the coin were indeed fair. As this

is less than 5% (p < 0.05), we can reject our hypothesis that the data fit the expected

distribution. In other words, we reject our null hypothesis with 95% confidence/certainty that

the coin was fair and we would expect a 50% heads : 50% tails ratio. We were testing the

distribution of a fair coin, so this suggests the coin was not fair, and the coach's accusation has

merit. This indicates that further tests should be conducted, and the number of trials (coin

flips) increased so a more definitive conclusion could be reached. Man, I love a good

controversy...

Stating conclusions

Once you have collected your data and analyzed them to get your p-value, you are ready to

determine whether your original hypothesis is supported or not. If the p-value in your analysis

is 0.05 or less (0.05, 0.01, etc.) then the data do not support your null hypothesis with 95%

confidence that the observed results would be obtained due to chance alone. So, as a scientist,

you would state your "unacceptable" results in this way:

"The differences observed in the data were statistically significant at the 0.05 level." You could

then add a statement like, "Therefore, with 95% confidence, the data do not support the

hypothesis that..."

Page 22 of 39


AP BiologyLab 01

This is how a scientist would state "acceptable" results:

"The differences observed in the data were not statistically significant at the 0.05 level." You

could then add a statement like, "Therefore, with 95% confidence, the data support the

hypothesis that..."

And you will see that over and over again in the conclusions of research papers.

Chi-Square Analysis

The Chi-square is a statistical test that makes a comparison between the data collected in an

experiment versus the data you expected to find. It can be used whenever you want to

compare the differences between expected results and experimental data.

Variability is always present in the real world. If you toss a coin 10 times, you will often get a

result different than 5 heads and 5 tails. The Chi-square test is a way to evaluate this variability

to get an idea if the difference between real and expected results are due to normal random

chance, or if there is some other factor involved (like our unbalanced coin). The Chi-square test

helps you to decide if the difference between your observed results and your expected results is

probably due to random chance alone, or if there is some other factor influencing the results.

In other words, it determines what our p-value is!

The Chi-square test will not, in fact, prove or disprove if random chance is the only thing

causing observed differences, but it will give an estimate of the likelihood that chance alone is

at work.

Determining the Chi-square Value

Chi-square is calculated based on the formula below.



We will fill out a table for the first go around so you can get familiar with how to use it. Follow

the following procedure to test the hypothesis that any given coin is even balanced and we

would expect to get the same number of heads (50) and tails (50) when flipped 100 times.

Activity

1. Restate the null and alternate hypotheses for this activity below:

H 0 : ____________________________________________________________________

_______________________________________________________________________

_______________________________________________________________________

H a : ____________________________________________________________________

_______________________________________________________________________

_______________________________________________________________________

Page 23 of 39


AP BiologyLab 01

2. Each team of two students will toss a pair of coins exactly 100 times and record the results

in Table 11. The only outcomes can be: both heads (H/H), one heads and one tails

(H/T), or both tails (T/T). Based on the laws of probability (that we learned in math

years ago), each of these have a 25%, 50%, and 25% chance of happening, respectively.

Each team must check their results to be certain that they have exactly 100 tosses.

Table 11: Team data for coin flip test.

Toss H/H H/T T/T Toss H/H H/T T/T Toss H/H H/T T/T

1 35 69

2 36 70

3 37 71

4 38 72

5 39 73

6 40 74

7 41 75

8 42 76

9 43 77

10 44 78

11 45 79

12 46 80

13 47 81

14 48 82

15 49 83

16 50 84

17 51 85

18 52 86

19 53 87

20 54 88

21 55 89

22 56 90

23 57 91

24 58 92

25 59 93

26 60 94

27 61 95

28 62 96

29 63 97

30 64 98

31 65 99

32 66 100

33 67

34 68

Total

(the sum of the total of each column must equal 100 tosses)

3. Record your team results on the computer and then record the summarized results on the

following page in Table 12.

Page 24 of 39


AP BiologyLab 01

Table 12: Class data for our coin flip test.

H/H

H/T

T/T

Total

1 2 3 4 5 6 7 8 9 10 Obs Exp

4. Analyze both the team and class data separately (in Tables 13 and 14) using the Chisquare

analysis as explained below.

A. For your individual team results, complete column A on Table 13 by entering your

observed results in the coin toss exercise.

B. For your individual team results, complete column B on Table 13 by entering your

expected results in the coin toss exercise. In some cases – but not this time – it is

okay if you have to use decimals for fractions (½ = 0.5).

C. For your individual team results, complete column C on Table 13 by calculating the

difference between your observed and expected results.

D. For your individual team results, complete column D on Table 13 by calculating the

square of the difference between your observed and expected results – this is done

to force the result to be a positive number.

E. For your individual team results, complete column E on Table 13 by dividing the

square in column D by the expected results.

F. Calculate the 2 value by summing each of the answers in column E. The symbol

means summation.

G. Repeat these calculations for the full class data and complete Table 14.

H. Enter the ―Degrees of Freedom‖ in Tables 13 and 14 based on the explanation

below the data tables.

Table 13: Chi-square analysis of individual team data.

H/H

A B C D E

obs exp obs – exp (obs – exp) 2 (obs – exp) 2

obs

H/T

T/T

2 total

Degrees of Freedom

Page 25 of 39


AP BiologyLab 01

Table 14: Chi-square analysis of class team data.

H/H

A B C D E

obs exp obs – exp (obs – exp) 2 (obs – exp) 2

obs

H/T

T/T

2 total

Degrees of Freedom

Interpreting Chi-Square Value

The rows in the Chi-square Distribution table (Table 15) refer to degrees of freedom. The

degrees of freedom are calculated as the one less than the number of possible results in your

experiment.

In the double coin toss exercise, you have 3 possible results: two heads, two tails, or one of

each. Therefore, there are two degrees of freedom for this experiment.

In a sense, the ―degrees of freedom‖ is measuring how many classes of results can ―freely‖

vary their numbers. In other words, if you have an accurate count of how many 2-heads, and

2-tails tosses were observed, then you already know how many of the 100 tosses ended up as

mixed head-tails, so the third measurement provides no additional information.

Table 15: The Chi-square distribution table.

degrees

of

freedom

probability value (p-value)

ACCEPT NULL HYPOTHESIS

REJECT

0.99 0.95 0.80 0.70 0.50 0.30 0.20 0.10 0.05 0.01

1 0.001 0.004 0.06 0.15 0.46 1.07 1.64 2.71 3.84 6.64

2 0.02 0.10 0.45 0.71 1.30 2.41 3.22 4.60 5.99 9.21

3 0.12 0.35 1.00 1.42 2.37 3.67 4.64 6.25 7.82 11.34

4 0.30 0.71 1.65 2.20 3.36 4.88 5.99 7.78 9.49 13.28

5 0.55 1.14 2.34 3.00 4.35 6.06 7.29 9.24 11.07 15.09

6 0.87 1.64 3.07 3.38 5.35 7.23 8.56 10.65 12.59 16.81

7 1.24 2.17 3.84 4.67 6.35 8.38 9.80 12.02 14.07 18.48

Page 26 of 39


AP BiologyLab 01

So which column do we use in the Chi-square Distribution table?

The columns in the Chi-square Distribution table with the decimals from 0.99 through 0.50 to

0.01 refer to probability levels of the Chi-square.

For instance, 3 events were observed in our coin toss exercise, so we already calculated we

would use 2 degrees of freedom. If we calculate a Chi-square value of 1.386 from the

experiment, then when we look this up on the Chi-square Distribution chart, we find that our

Chi-square value places us near the ―p=.50‖ column – in the range of 0.50 > 0.30. This means

that the variance between our observed results and our expected results would occur from

random chance between 30-50% of the time. Therefore, we could conclude (with 95%

confidence – 2 standard deviation interval) that chance alone could cause such a variance often

enough that the data still supported our hypothesis, and probably another factor is not

influencing our coin toss results.

However, if our calculated Chi-square value, yielded a sum of 5.991 or higher, then when we

look this up on the Chi-square Distribution chart, we find that our Chi-square value places us

beyond the ―p=.05‖ column. This means that the variance between our observed results and

our expected results would occur from random chance alone less than 5% of the time (only 1

out of every 20 times). Therefore, we would conclude (with 95% confidence) that chance

factors alone are not likely to be the cause of this variance. Some other factor is causing some

coin combinations to come up more than would be expected. Maybe our coins are not balanced

and are weighted to one side more than another.

Variations on the Chi-Square Analysis

In medical research, the chi-square test is used in a similar — but interestingly different — way.

When a scientist is testing a new drug, the experiment is set up so that the control group

receives a placebo and the experimental group receives the new drug. Analysis of the data is

trying to see if there is a difference between the two groups. The expected values would be

that equal numbers of people get better in the two groups — which would mean that the drug

has no effect. If the chi-square test yields a p-value greater than .05, then the scientist would

accept the null hypothesis which would mean the drug has no significant effect. The differences

between the expected and the observed data could be due to random chance alone. If the chisquare

test yields a p-value = .05, then the scientist would reject the null hypothesis which

would mean the drug has a significant effect. The differences between the expected and the

observed data could not be due to random chance alone and can be assumed to have come

from the drug treatment.

In fact, chi-square analysis tables can go to much lower p-values than the one above — they

could have p-values of .001 (1 in 1000 chance), .0001 (1 in 10,000 chance), and so forth. For

example, a p-value of .0001 would mean that there would only be a 1 in 10,000 chance that

the differences between the expected and the observed data were due to random chance

alone, whereas there is a 99.99% chance that the difference is really caused by the treatment.

These results would be considered highly significant.

Page 27 of 39


AP BiologyLab 01

Chi-Square Analysis Questions


Based on the laws of probability, what was your hypothesis (expected numbers) for your

individual team coin toss?

__________________________________________________________________________

__________________________________________________________________________


What was your calculated Chi-square value for your individual team data? _____________

What p-value does this Chi-square value correspond to? _____________


Was your hypothesis supported by your results? Explain (completely and correctly) using

the Chi-square analysis.

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________


Based on the laws of probability, what was your hypothesis (expected numbers) for the

class coin toss?

__________________________________________________________________________

__________________________________________________________________________

What was your calculated Chi-square value for the class data? _____________

What p-value does this Chi-square value correspond to? _____________


Was your hypothesis supported by your results? Explain (completely and correctly) using

the Chi-square analysis.

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

Page 28 of 39


AP BiologyLab 01

Significance of a Large Sample Size

When designing research studies, scientists purposely choose large sample sizes. Work through

these scenarios to understand why…

a) Just an in your experiment, you flipped 2 coins, but you only did it 10 times. You collected

these data below. Use Table 16 to calculate the Chi-square value. (It is okay to use

decimals for your expected column!)

H/H 1

H/T 8

T/T 1

Table 16: Testing coin flipping results with a sample size of 10 flips.

obs exp obs – exp (obs – exp) 2 (obs – exp) 2

2 total

Degrees of Freedom

obs


Would you accept or reject the null hypothesis? Explain (completely and correctly) using

the Chi-square analysis.

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

b) Now you flipped your coins again, but you did it 100 times. You collected these data below.

Use Table 17 to calculate the Chi-square value.

H/H 10

H/T 80

T/T 10

Table 17: Testing coin flipping results with a sample size of 100 flips.

obs exp obs – exp (obs – exp) 2 (obs – exp) 2

2 total

Degrees of Freedom

obs

Page 29 of 39


AP BiologyLab 01


Would you accept or reject the null hypothesis? Explain (completely and correctly) using

the Chi-square analysis.

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

c) Now you flipped your coins again, but you did it 1000 times. You collected these data

below. Use Table 18 to calculate the Chi-square value.

H/H 100

H/T 800

T/T 100

Table 18: Testing coin flipping results with a sample size of 1000 flips.

obs exp obs – exp (obs – exp) 2 (obs – exp) 2

2 total

Degrees of Freedom

obs


Would you accept or reject the null hypothesis? Explain (completely and correctly) using

the Chi-square analysis.

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

d) Explain why scientists purposely choose large sample sizes when they design research

studies using data obtained from each of the above analyses.

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

Page 30 of 39


AP BiologyLab 01

Spectrophotometry

Many kinds of molecules interact with or absorb specific types of radiant energy in a predictable

fashion. For example, when while light illuminates an object, the color that the eye perceives is

determined by the absorption by the object of one or more of the colors from the source of the

white light. The remaining wavelength(s) are reflected (or transmitted) as a specific color.

Thus an object that appears red absorbs the blue or green colors of light (or both), but not the

red.

The perception of color, as just described is qualitative. It indicates what is happening but says

nothing about the extent to which the event is taking place. The eye is not a quantitative

instrument. However, there are instruments, called spectrophotometers, that electronically

quantify the amount and kinds of light that are absorbed by molecules in solution. In its

simplest form, a spectrophotometer has a source of white light (for visible spectrophotometry—

some use UV) that is focused on a prism or diffraction grating to separate the white light into its

individual bands of radiant energy. Each wavelength (color) is then selectively focused through

a narrow slit. The width of this slit is important to the precision of the measurement; the

narrower the slit, the more closely absorption is related to a specific wavelength of light.

Conversely, the broader the slit, the more light of different wavelengths passes through, which

results in a reduction in the precision of the measurement. This monochromatic (single

wavelength) beam of light, called the incident beam (I O ), then passes through the sample

being measured. The sample, usually dissolved in a suitable solvent, is contained in a an

optically selected cuvette, which should be standardized to have a light path 1 cm across.

After passing through the sample, the selected wavelength of light (no referred to as the

transmitted beam, (I) strikes a photoelectric tube. If the substance in the cuvette has

absorbed any of the incident light, the transmitted light will then be reduced in total energy

content. If the substance in the sample container does not absorb any of the incident beam,

the radiant energy of the transmitted beam will then be about the same amount as that of the

incident beam. When the transmitted beam strikes the photoelectric tube, it generates an

electric current proportional to the intensity of the light energy striking it. By connecting the

photoelectric tube to a device that measures electric current (a galvanometer), a means of

directly measuring the intensity of the transmitted beam is achieved. In the

spectrophotometers that we will use (called the Spec-20), the galvanometer has two scales:

one indicates the % transmittance (%T), and the other, a logarithmic scale with unequal

divisions graduated from 0.0 to 2.0, indicates the absorbance (A).

Because most biological molecules are dissolved in a solvent before measurement, a source of

error can be due to the possibility that the solvent itself absorbs light. To assure that the

spectrophotometric measurement will reflect only the light absorption of the molecules being

studied, a mechanism for ―subtracting‖ the absorbance of the solvent is necessary. To achieve

this, a ―blank‖ (the solvent) is first inserted into the instrument, and the scale is set to read

100% transmittance (or 0.0 absorbance) for the solvent. The ―sample,‖ containing the solute

plus the solvent, is then inserted into the instrument. Any reading on the scale that is less than

100% T (or greater than 0.0 A) is considered to be due to absorbance by the solute only.

As mentioned earlier, spectrophotometers are not limited to detecting absorption of only visible

light. Some also have a source of ultraviolet light (usually supplied by a hydrogen or mercury

lamp), which has wavelengths that range from about 180 to 400 nm. Ultraviolet wavelengths

ranging from 180 to 350 nm are particularly useful in studying such biological molecules as

amino acids, proteins, and nucleic acids because each of these compounds have characteristic

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AP BiologyLab 01

absorbances at different UV wavelengths. Other spectrophotometers use infrared radiation

(from 780 to 25,000 nm) as well.

Units of Measurement

The following terminology is commonly used in spectrophotometry.



Transmittance (T): the ratio of the transmitted light (I) of the sample to the incident light

(I O ) on the sample.

T =

This value is multiplied by 100 to derive the % T. For example:

% T =

75

100

I

I O

= 0.75

Absorbance (A): logarithm to the base 10 of the reciprocal of the transmittance:

For example:

A = log 10

1. Suppose a % T of 50 was recorded (equivalent to T = 0.50).

2. Then A = log 10 (1/0.50) = log 10 2.0.

3. Thus log 10 2.0 = 0.301 (A equivalent to a % T of 50).

4. Similarly a % T of 25 = 0.602 A; a % T of 75 = 0.125 A; and so forth.

The absorbance scale is normally present along with the transmittance scale on

spectrophotometers. The chief usefulness of absorbance lies in the fact that it is a logarithmic

rather than arithmetic function, allowing the use of the Lambert-Beer law (Beer‘s Law), which

states that for a given concentration range the concentration of solute molecules is directly

proportional to absorbance. This law can be expressed as

log 10

I O

1

T

= A

in which I O is the intensity of the incident light; I is the intensity of the transmitted light.

I

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AP BiologyLab 01

Figure 6: Transmittance and Aborbance of a sample at a given wavelength at various concentrations.

The usefulness of absorbance can be seen in the graphs shown in Figure 6, one graph

showing the percent transmittance plotted against concentration and the other showing

absorbance plotted against concentration. Using the Lambert-Beer relationship, it is necessary

to plot only three or four points to obtain the straight-line relationship shown in the bottom

graph of the two. However, certain conditions must prevail for the Lambert-Beer relationship to

hold:

1. Monochromatic light is used.

2. A max is used (i.e., the wavelength maximally absorbed by the substance being

analyzed).

3. The quantitative relationship between absorbance and concentration can be

established.

The first condition can be met by using a prism or diffraction grating or other device that can

disperse visible light into its spectra.

The second condition can be met by determining the absorption spectrum of the compound.

This is done by plotting the absorbance of the substance at a number of different wavelengths.

The wavelength at which absorbance is greatest is called the A max (or max ) and is the most

satisfactory wavelength to use because, on the slope, absorbance changes rapidly with slight

wavelength deviations, whereas at the maximum absorbance, changes in wavelength alter

absorbance less. Figure 7 shows an absorption spectrum of a hypothetical substance having

an A max of approximately 650 nm. Some compounds, however, can have several peaks both in

the visible spectrum and in the ultraviolet range. An example of this is shown in Figure 8 for

riboflavin.

Figure 7: Absorption spectrum of a known concentration of a sample.

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AP BiologyLab 01

Figure 8: Absorption spectrum of riboflavin.

To establish the quantitative relationship between absorbance and concentration of the colored

substance, it is necessary to prepare a series of standards of the substance analyzed in graded

known concentrations (a.k.a. color standards). Because absorbance is directly proportional to

concentration, a plot of absorbance versus concentration of the standard yields a straight line.

Such a plot is called a standard curve or calibration curve as shown in Figure 9. After

several points have been plotted, the intervening points can be extrapolated by connecting the

known points with a straight line. It is not necessary to use dotted lines to indicate

extrapolation on graphs; a dotted line is used in the illustration to indicate the parts of the line

for which points were not determined but were presumed. When the Lambert-Beer law is

followed, this is an acceptable and time-saving assumption; otherwise, points would be need to

be plotted throughout the entire line. In general, your graph should extend from a minimum of

about 0.025 A to a maximum of about 1.0 A, or from 94% T to 10% T, this being the

―readable‖ and reproducible values of absorbance. However, recall that the Lambert-Beer law

operates at only certain concentrations. This is apparent in Figure 9, in which, at

concentrations greater than 1.0 mg/mL, the curve slopes, indicating the loss of the

concentration-absorbance relationship.

Figure 9: Absorption curve extended from known values.

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AP BiologyLab 01

After a concentration curve for a given substance has been established, it is relatively easy to

determine the quantity of that substance in a solution of unknown concentration by determining

the absorbance of the unknown an locating it on the y-axis, or ordinate (Figure 10). A straight

line is then drawn parallel to the x-axis, or abscissa, until it intersects with the experimental

curve. A perpendicular is then dropped to the x-axis, the value at the point of intersection

indicating the concentration of the unknown solution. In this example, the unknown

absorbance is 0.32, which indicates a concentration of about 0.37 mg. Concentrations are

commonly expressed either as micrograms per milliliter (g/mL) or as milligrams per milliliter

(mg/mL).

Figure 10: Using a standard curve to find the concentration of an unknown sample.

If the absorbance value of the unknown is such that the line drawn parallel to the x-axis

intersects the experimental curve where it is curved (Figure 9), then you cannot accurately

determine the concentration. In this event, dilute the unknown by some factor until the

absorbance readings intersect with the straight-line part of the graph where concentration is

proportional to absorbance. You can then determine the unknown concentration and multiply

the value by the dilution factor.

Quantitative Chemical Determination of Protein

In protein molecules, the successive amino acid molecules are bonded together between the

carboxyl and amino groups of adjacent amino acids to from long, unbranched molecules. Such

bonds are commonly called peptide bonds. The structures resulting from the formation of

peptide bonds are called dipeptides, tripeptides, or polypeptides, depending on the number of

amino acids involved. The individual amino acids are called residues.

Biuret, a simple molecule prepared from urea, contains what may be regarded as two peptide

bonds and thus is structurally similar to simple peptides. This molecule when treated with

copper sulfate in alkaline solution (the biuret reaction) gives an intense purple color. The

reaction is based on the formation of a purple-colored complex between copper ions and two or

more peptide bonds. Proteins give a particularly strong biuret reaction because they contain a

large number of peptide bonds. The biuret reaction may be used to quantitate the

concentration of proteins because peptide bonds occur with approximately the same frequency

per gram of material for most proteins.

In this experiment, you will determine the concentration of unknown protein solutions by

measuring colorimetrically the intensity of their color production in the biuret reaction as

compared with the color produced by a known concentration of the protein albumin.

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AP BiologyLab 01

Calibration of the Spectrophotometer

Figure 11: Diagram of a spectrophotometer.

1. Rotate the wavelength control (1) shown in Figure 11 until the desired wavelength is

shown on the wavelength dial (2). The wavelength for a given substance can be found by

referring to the literature or by determining it experimentally.

2. Turn the instrument on by rotating the ―0‖ control (3) in a clockwise position. Allow at least

5 minutes for the instrument to warm up.

3. Adjust the ―0‖ control with the cover of the sample holder closed (5) until the needle is at 0

on the tranmittance scale (4).

4. Place a cuvette containing water or another solvent in the sample holder, and close the

cover.

5. Rotate the light control (6) so that the needle is at 100 on the transmittance scale (0.0

absorbance). This control regulates the amount of light passing through the second slit

through the phototube.

6. The unknown samples may then be placed in the tube holder, and the percentage of

transmittance or absorbance can be read. The needle should always return to zero when

the tube is removed. Check the 0% and 100% transmittance occasionally with the solvent

tube in the sample holder to make certain the unit is calibrated. Note: Always check the

wavelength scale to be certain that the desired wavelength is being used.

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AP BiologyLab 01

Using a Standard Curve to Determine an Unknown

7. Prepare a set of six test tubes, containing increasing amounts of a standard solution of

albumin and 0.5 M KCl, as shown in Table 19.

Table 19: Preparing a standard curve for albumin.

Tube

Number

Albumin

2.5 mg/mL

(mL)

Total

protein

content

(mg)

0.5 M KCl

(mL)

Biuret

reagent

(mL)

%T A 540

1 0.0 0.0 5.0 0.5 100 0.0

2 1.0 2.5 4.0 0.5

3 2.0 5.0 3.0 0.5

4 3.0 7.5 2.0 0.5

5 4.0 10.0 1.0 0.5

6 5.0 12.5 0.0 0.5

( ) --- 0.5

( ) --- 0.5

8. Add 0.5 mL of biuret reagent to each of the tubes, and mix thoroughly by rotating the tubes

between your palms. The color is fully developed in 20 minutes and is stable for at least an

hour. While waiting for the color to develop, calibrate your instrument at 540 nm using

Tube 1—which is a blank containing all reagents except protein.

9. Read Tubes 2 through 6 against it. Record your readings in Table 19. Covert the % T to

absorbance (A) for Tubes 1 through 6 using Table 20 and then plot your data to obtain a

standard curve for albumin. Do this on the graph paper provided, and also use Excel to do

the same (for Excel, do a regression analysis to check the reliability of your standard curve).

10. Using the standard curve, determine the concentrations of your unknown protein solutions

using both your handwritten graph and the Excel spreadsheet/graph.

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AP BiologyLab 01

Table 20: Absorbance vs. Transmittance.

%T Absorbance (A) %T Absorbance (A)

.25 .50 .75 .25 .50 .75

1 2.000 1.903 1.824 1.757 51 .2924 .2903 .2882 .2861

2 1.699 1.648 1.602 1.561 52 .2840 .2819 .2798 .2777

3 1.532 1.488 1.456 1.426 53 .2756 .2736 .2716 .2696

4 1.398 1.372 1.347 1.323 54 .2676 .2656 .2636 .2616

5 1.301 1.280 1.260 1.240 55 .2596 .2577 .2557 .2537

6 1.222 1.204 1.187 1.171 56 .2518 .2499 .2480 .2460

7 1.155 1.140 1.126 1.112 57 .2441 .2422 .2403 .2384

8 1.097 1.083 1.071 1.059 58 .2366 .2347 .2328 .2319

9 1.046 1.034 1.022 1.011 59 .2291 .2273 .2255 .2236

10 1.000 .989 .979 .969 60 .2218 .2200 .2182 .2164

11 .959 .949 .939 .930 61 .2147 .2129 .2111 .2093

12 .921 .912 .903 .894 62 .2076 .2059 .2041 .2024

13 .886 .878 .870 .862 63 .2007 .1990 .1973 .1956

14 .854 .846 .838 .831 64 .1939 .1922 .1905 .1888

15 .824 .817 .810 .803 65 .1871 .1855 .1838 .1821

16 .796 .789 .782 .776 66 .1805 .1788 .1772 .1756

17 .770 .763 .757 .751 67 .1739 .1723 .1707 .1691

18 .745 .739 .733 .727 68 .1675 .1659 .1643 .1627

19 .721 .716 .710 .704 69 .1612 .1596 .1580 .1565

20 .699 .694 .688 .683 70 .1549 .1534 .1517 .1503

21 .678 .673 .668 .663 71 .1487 .1472 .1457 .1442

22 .658 .653 .648 .643 72 .1427 .1412 .1397 .1382

23 .638 .634 .629 .624 73 .1367 .1652 .1337 .1322

24 .620 .615 .611 .606 74 .1308 .1293 .1278 .1264

25 .602 .598 .594 .589 75 .1249 .1235 .1221 .1206

26 .585 .581 .577 .573 76 .1192 .1177 .1163 .1149

27 .569 .565 .561 .557 77 .1135 .1121 .1107 .1093

28 .553 .549 .545 .542 78 .1079 .1065 .1051 .1037

29 .538 .534 .530 .527 79 .1024 .1010 .0996 .0982

30 .532 .520 .516 .512 80 .0969 .0955 .0942 .0928

31 .509 .505 .502 .496 81 .0975 .0901 .0888 .0875

32 .495 .491 .488 .485 82 .0862 .0848 .0835 .0822

33 .482 .478 .475 .472 83 .0809 .0796 .0783 .0770

34 .469 .465 .462 .459 84 .0757 .0744 .0731 .0718

35 .456 .453 .450 .447 85 .0706 .0693 .0680 .0667

36 .444 .441 .438 .435 86 .0655 .0672 .0630 .0617

37 .432 .429 .426 .423 87 .0605 .0593 .0580 .0568

38 .420 .417 .414 .412 88 .0555 .0543 .0531 .0518

39 .409 .406 .403 .401 89 .0505 .0494 .0482 .0470

40 .398 .395 .392 .390 90 .0458 .0446 .0434 .0422

41 .387 .385 .385 .380 91 .0410 .0396 .0386 .0374

42 .377 .374 .372 .369 92 .0362 .0351 .0339 .0327

43 .367 .364 .362 .359 93 .0315 .0304 .0292 .0281

44 .357 .354 .352 .349 94 .0269 .0257 .0246 .0235

45 .347 .344 .342 .340 95 .0223 .0212 .0200 .0188

46 .337 .335 .332 .330 96 .0177 .0166 .0155 .0144

47 .328 .325 .323 .321 97 .0132 .0121 .0110 .0099

48 .319 .317 .314 .312 98 .0088 .0077 .0066 .0055

49 .310 .308 .305 .303 99 .0044 .0033 .0022 .0011

50 .301 .299 .297 .295 100 .0000 .0000 .0000 .0000

Note: Intermediate values may be arrived at by using the .25, .50, and .75 columns. For

example, if %T equals 85, the absorbance equals .0706. If %T equals 85.75 the absorbance

equals .0067

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AP BiologyLab 01

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