Lies, Damned Lies, or Statistics- How to Tell the Truth with Statistics, 2017a

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10 1. ONE-VARIABLE STATISTICS: BASICS Pie charts are widely used, but actually they are almost never a good choice. In fact, do an Internet search for the phrase “pie charts are bad” and there will be nearly 3000 hits. Many of the arguments are quite insightful. When you see a pie chart, it is either an attempt (misguided, though) by someone to be folksy and friendly, or it is a sign that the author is quite unsophisticated with data visualization, or, worst of all, it might be a sign that the author is trying to use mathematical methods in a deceptive way. In addition, all of the cautions we mentioned above for bar charts of categorical data apply, mostly in exactly the same way, for pie charts.
1.3. VISUAL REPRESENTATION OF DATA, II 11 1.3. Visual Representation of Data, II: Quantitative Variables Now suppose we have a population and quantitative variable in which we are interested. We get a sample, which could be large or small, and look at the values of the our variable for the individuals in that sample. There are two ways we tend to make pictures of datasets like this: stem-and-leaf plots and histograms. 1.3.1. Stem-and-leaf Plots. One somewhat old-fashioned way to handle a modest amount of quantitative data produces something between simply a list of all the data values and a graph. It’s not a bad technique to know about in case one has to write down a dataset by hand, but very tedious – and quite unnecessary, if one uses modern electronic tools instead – if the dataset has more than a couple dozen values. The easiest case of this technique is where the data are all whole numbers in the range 0 − 99. In that case, one can take off the tens place of each number – call it the stem – and put it on the left side of a vertical bar, and then line up all the ones places – each is a leaf – to the right of that stem. The whole thing is called a stem-and-leaf plot or, sometimes, just a stemplot. It’s important not to skip any stems which are in the middle of the dataset, even if there are no corresponding leaves. It is also a good idea to allow repeated leaves, if there are repeated numbers in the dataset, so that the length of the row of leaves will give a good representation of how much data is in that general group of data values. EXAMPLE 1.3.1. Here is a list of the scores of 30 students on a statistics test: 86 80 25 77 73 76 88 90 69 93 90 83 70 73 73 70 90 83 71 95 40 58 68 69 100 78 87 25 92 74 As we said, using the tens place (and the hundreds place as well, for the data value 100)as the stem and the ones place as the leaf, we get TABLE 1.3.1.1. Stem-and-leaf plot of students’ scores, Key: 1|7 =17 Stem Leaf 10 0 9 000235 8 033678 7 0013334678 6 899 5 8 4 0 3 2 55
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Page 12 and 13: The first instinct of the scientist
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Part 3 Inferential Statistics
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CHAPTER 6 Basic Inferences The purp
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6.1. THE CENTRAL LIMIT THEOREM 113
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6.2. BASIC CONFIDENCE INTERVALS 115
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The two hypotheses then are 6.3. BA
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EXERCISES 125 Exercises EXERCISE 6.
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Bibliography [Gal17] Gallup, Presid
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130 INDEX causation, 46 center of a
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Lies, Damned Lies, or Statistics- How to Tell the Truth with Statistics, 2017a

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