1. Review As sample size increases, the distribution of sample means A.Becomes wider and more normally shaped B.Becomes narrower and more normally shaped C.Becomes wider and less normally shaped D.Becomes narrower and less normally shaped

2. Review A population has a mean of µ = 10 and a standard deviation of  = 5. You plan to sample 100 people. What’s the expected value of your sample mean, M? A.0.5 B.1 C.5 D.10

3. Review A population has a mean of µ = 10 and a standard deviation of  = 5. You plan to sample 100 people. The expected (average) value of M is equal to µ, but M for any specific sample will be off, either too high or too low. How far from µ will the sample mean typically be? A.0.5 B.1 C.5 D.10

4. One-sample t-test 10/7

5. Hypothesis Test for Population Mean Goal: Infer  from M Null hypothesis (H 0 ):  =  0 – Usually 0 – Sometimes another value, e.g. from larger population Change scores – Memory improvement, weight loss, etc. Sub-population within known, larger population – IQ of CU undergrads Approach: – Determine likelihood function for M, using CLT – Compare actual sample mean to critical value

6. Likelihood Function for M Probability distribution for M, according to H 0 Central Limit Theorem: – Mean equals population mean:  M =  0 – Standard deviation: – Shape: Normal 00 Probability MM p(M)p(M)

7. Critical Value for M Result that has 5% (  ) chance of being exceeded, IF null hypothesis is true Easier to find after standardizing p(M) – Convert distribution of sample means to z-scores: Critical value for z M always the same – Just use qnorm() – Equals 1.64 for  = 5% M crit 00 Probability MM  p(M)p(M) z crit  Probability   p(zM)p(zM)

8. Example: IQ Are CU undergrads smarter than population? – Sample size n = 100, sample mean M = 103 Likelihood function – If no difference, what are probabilities of sample means? – Null hypothesis:  0 = 100 – CLT: z-score: (103-100)/1.5 = 2 M  M = 100  M =  /√n = 15/10 = 1.5 zMzM z crit

9. Problem: Unknown Variance Test statistic depends on population parameter – Can only depend on data or values assumed by H 0 Could include  in null hypothesis – H 0 :  =  0 &  =  0 – Usually no theoretical basis for choice of  0 – Cannot tell which assumption fails Change test statistic – Replace population SD with sample SD – Depends only on data and  0 –.–. ?

10. t Statistic Invented in 1908 by “Student” at Guinness brewery. Deviation of sample mean divided by estimated standard error Depends only on data and  0 Sampling distribution depends only on n

11. t Distribution Sampling distribution of t statistic Derived from ratio of Normal and (modified)  2 Depends only on sample size – Degrees of freedom: df = n – 1 – Invariant with respect to  Shaped like Normal, but with fatter tails – Reflects uncertainty in sample variance – Closer to Normal as n increases Critical value decreases as n increases  =.05 dft crit dft crit 16.3152.02 22.92101.81 32.35301.70 42.13∞1.64

12. Steps of t-test 1. State clearly the two hypotheses 2. Determine null and alternative hypotheses H 0 :  =  0 H 1 :  ≠  0 3. Compute the test statistic t from the data. 4. Determine likelihood function for test statistic according to H 0 t distribution with n-1 degrees of freedom 5. Find critical value R: qt(alpha,n-1,lower.tail=FALSE) 6. Compare actual result to critical value t < t crit : Retain null hypothesis, μ = μ 0 t > t crit : Reject null hypothesis, μ ≠ μ 0

13. Example: Rat Mazes Measure maze time on and off drug – Difference score: Time drug – Time no drug – Data (seconds): 5, 6, -8, -3, 7, -1, 1, 2 Sample mean: M = 1.0 Sample standard deviation: s = 5.06 Standard error: SE = s/√n = 5.06/2.83 = 1.79 t = (M –  0 )/SE = (1.0 – 0)/1.79 =.56 Critical value: qnorm(.05,7,lower.tail=FALSE) = 1.89 t does not exceed critical value – Cannot reject null hypothesis – Assume no effect of drug

14. Review You run a t-test and get a result of t = 7. What is your conclusion? A.Reject the null hypothesis because t is bigger than expected by chance B.Reject the null hypothesis because t is smaller than expected by chance C.Keep the null hypothesis because t is bigger than expected by chance D.Keep the null hypothesis because t is smaller than expected by chance

15. Review You run a t-test and get a result of t = 2. What does the value of 2 mean in this case? A.The alternative hypothesis is twice as likely to be true as the null hypothesis B.The sample mean is twice as far from µ 0 as you would expect by chance C.The population mean is likely within ±2 of the sample mean D.Your chance of a Type I error will double if you reject the null hypothesis

16. Review Your null hypothesis predicts the population mean should be µ 0 = 0. You measure 16 people and calculate statistics of M = 4 and s = 32. What is the value of your t statistic? A.2 B.0.5 C.0.25 D.4