Statistics and Hypothesis Testing

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Size of a test: probability of erring in rejection By pure sampling chance, one time out of twenty, or 5 percent of the time, the data will reject the null hypothesis even though the null hypothesis is true. For more sensitive matters than mere social science, a higher st<strong>and</strong>ard (lower p-value) may be required.
An example Is the mean wage among recent college graduates $20 per hour H 0 : E(Y ) = µ Y ,0 H 0 : E(hourly earnings) = $20 per hour In a sample of n = 200 recent college graduates, the sample average wage, Y act = $22.64. STOP RIGHT HERE. Why doesn’t this prove immediately that the mean wage among recent college graduates is $22.64, obviously above $20
Page 1 and 2: Statistics and Hypothesis Testing M
Page 3 and 4: Summary of Main Points ◮ We will
Page 5 and 6: What would be a good way to guess
Page 7 and 8: Y is the least squares estimator of
Page 9 and 10: Random Sampling The consequences of
Page 11 and 12: Hypothesis Testing 1. Gather a samp
Page 13 and 14: The p-Value p-value ≡ Pr H 0 [
Page 15 and 16: How likely is the observed value In
Page 17 and 18: Sample Variance, Sample Standard De
Page 19 and 20: Sample Standard Deviation The sampl
Page 21 and 22: Summary: Why standard error ◮ Can
Page 23: The Prespecified Significance Level
Page 27 and 28: Average wage example, continued ◮
Page 29 and 30: Type I and Type II errors Null Hypo
Page 31 and 32: From Hypothesis Testing to Confiden
Page 33 and 34: Example: 95 percent CI In a sample
Page 35: Next time we do something from the

Statistics and Hypothesis Testing

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