1. Hypothesis Testing Quantitative Methods in HPELS 440:210

2. Agenda Introduction Hypothesis Testing  General Process Errors in Hypothesis Testing One vs. Two Tailed Tests Effect Size and Power Instat Example

3. Introduction  Hypothesis Testing Recall:  Inferential Statistics: Calculation of sample statistic to make predictions about population parameter  Two potential problems with samples: Sampling error Variation between samples  Infinite # of samples  predictable pattern  sampling distribution Normal µ = µ M  M =  /√n

4. Introduction  Hypothesis Testing Common statistical procedure Allows for comparison of means General process: 1. State hypotheses 2. Set criteria for decision making 3. Collect data  calculate statistic 4. Make decision

5. Introduction  Hypothesis Testing Remainder of presentation will use following concepts to perform a hypothesis test:  Z-score  Probability  Sampling distribution

6. Agenda Introduction Hypothesis Testing  General Process Errors in Hypothesis Testing One vs. Two Tailed Tests Effect Size and Power Instat Example

7. General Process of HT Step 1: State hypotheses Step 2: Set criteria for decision making Step 3: Collect data and calculate statistic Step 4: Make decision

8. Step 1: State Hypotheses Two types of hypotheses: 1. Null Hypothesis (H 0 ): 2. Alternative Hypothesis (H 1 ):  Directional  Non-directional Only one can be true Example 8.1, p 223

9. Assume the following about 2-year olds: µ = 26  = 4  M =  /√n = 1 n = 16 Researchers want to know if extra handling/stimulation of babies will result in increased body weight once the baby reaches 2 years of age

10. Null Hypothesis: H 0 : Sample mean = 26 Alternative Hypothesis: H 1 : Sample mean ≠ 26

11. Assume that this distribution is the “TRUE” representation of the population Recall: If an INFINITE number of samples are taken, the SAMPLING DISTRIBUTION will be NORMAL with µ = µ M and will be identical to the population distribution Reality: Only ONE sample will be chose What is the probability of choosing a sample with a mean (M) that is 1, 2, or 3 SD above or below the mean (µ M )? µMµM

12. µMµM µMµM µMµM p(M > µ M + 1  ) = 15.87%p(M > µ M + 2  ) = 2.28% p(M > µ M + 3  ) = 0.13%

13. It is much more PROBABLE that our sample mean (M) will fall closer to the mean of the means (µ M ) as well as the population mean (µ) µMµM Inferential statistics is based on the assumption that our sample is PROBABLY representative of the population

14. Our sample could be here,or here, but we assume that it is here! µMµM

15. H 0 : Sample mean = 26 If true (no effect): 1.) It is PROBABLE that the sample mean (M) will fall in the middle 2.) It is IMPROBABLE that the sample mean (M) will fall in the extreme edges H 1 : Sample mean ≠ 26 If true (effect): 1.) It is PROBABLE that the sample mean (M) will fall in the extreme edges 2.) It is IMPROBABLE that the sample mean (M) will fall in the middle

16. Assume that M = 30 lbs (n = 16) µ = 26M = 30 Accept or reject? H 0 : Sample mean = 26 What criteria do you use to make the decision?

17. Step 2: Set Criteria for Decision A sampling distribution can be divided into two sections:  Middle: Sample means likely to be obtained if H 0 is accepted  Ends: Sample means not likely to be obtained if H 0 is rejected Alpha (  ) is the criteria that defines the boundaries of each section

18. Step 2: Set Criteria for Decision Alpha:  AKA  level of significance Ask this question:  What degree of certainty do I need to reject the H 0 ? 90% certain:  = 0.10 95% certain:  = 0.05 99% certain:  = 0.01

19. Step 2: Set Criteria for Decision As level of certainty increases:   decreases  Middle section gets larger  Critical regions (edges) get smaller Bottom line: A larger test statistic is needed to reject the H 0

20. Step 2: Set Criteria for Decision Directional vs. non- directional alternative hypotheses Directional:  H1: M > or < X Non-directional:  H1: M ≠ X Which is more difficult to reject H 0 ?

21. Step 2: Set Criteria for Decision Z-scores represent boundaries that divide sampling distribution Non-directional:   = 0.10 defined by Z = 1.64   = 0.05 defined by Z = 1.96   = 0.01 defined by Z = 2.57 Directional:   = 0.10 defined by Z = 1.28   = 0.05 defined by Z = 1.64   = 0.01 defined by Z = 2.33

22. Critical Z-Scores  Non-Directional Hypotheses 90% 95% 99% Z=1.64 Z=1.96 Z=2.58 Z=1.64 Z=1.96 Z=2.58

23. Critical Z-Scores  Directional Hypotheses Z=1.28 Z=1.64 Z=2.34 90% 95% 99%

24. Step 2: Set Criteria for Decision Where should you set alpha?  Exploratory research  0.10  Most common  0.05  0.01 or lower?

25. Step 3: Collect Data/Calculate Statistic Z = M - µ /  M where:  M = sample mean  µ = value from the null hypothesis H 0 : sample = X   M =  /√n Note: Population  must be known otherwise the Z-score is an inappropriate statistic!!!!!

26. Example 8.1  Continued Step 3: Collect Data/Calculate Statistic

27. Assume the following about 2-year olds: µ = 26  = 4  M =  /√n = 1 n = 16 Researchers want to know if extra handling/stimulation of babies will result in increased body weight once the baby reaches 2 years of age M = 30 Z = M - µ /  M Z = 30 – 26 / 1 Z = 4 / 1 = 4.0

28. Process: 1. Draw a sketch with critical Z-score  Assume non-directional  Alpha = 0.05 2. Plot Z-score statistic on sketch 3. Make decision Step 4: Make Decision

29. µ = 26 M = 30 Z = 4.0 Step 1: Draw sketch Critical Z-score Z = 1.96 Critical Z-score Z = 1.96 Step 3: Make Decision: Z = 4.0  falls inside the critical region If H 0 is false, it is PROBABLE that the Z-score will fall in the critical region ACCEPT OR REJECT? Step 2: Plot Z-score

30. Agenda Introduction Hypothesis Testing  General Process Errors in Hypothesis Testing One vs. Two Tailed Tests Effect Size and Power Instat Example

31. Errors in Hypothesis Testing Recall  Problems with samples:  Sampling error  Variability of samples Inferential statistics use sample statistics to predict population parameters There is ALWAYS chance for error

32. Errors in Hypothesis Testing There is potential for two kinds of error: 1. Type I error 2. Type II error

33. Type I Error Rejection of a true H 0 Recall  alpha = certainty of rejecting H 0  Example: Alpha = 0.05 95% certain of correctly rejecting the H 0 Therefore 5% certain of incorrectly rejecting the H 0 Alpha maybe thought of as the “probability of making a Type I error Consequences:  False report  Waste of time/resources

34. Type II Error Acceptance of a false H 0 Consequences:  Not as serious as Type I error  Researcher may repeat experiment if type II error is suspected

36. Agenda Introduction Hypothesis Testing  General Process Errors in Hypothesis Testing One vs. Two Tailed Tests Effect Size and Power Instat Example

37. One vs. Two-Tailed Tests One-Tailed (Directional) Tests:  Specify an increase or decrease in the alternative hypothesis  Advantage: More powerful  Disadvantage: Prior knowledge required

38. One vs. Two-Tailed Tests Two-Tailed (Non-Directional) Tests:  Do not specify an increase or decrease in the alternative hypothesis  Advantage: No prior knowledge required  Disadvantage: Less powerful

39. Agenda Introduction Hypothesis Testing  General Process Errors in Hypothesis Testing One vs. Two Tailed Tests Effect Size and Power Instat Example

40. Statistical Software  p-value The p-value is the probability of a type I error Recall alpha (  ) Recall  Step 4: Make a Decision

41. If the p-value >   accept the H 0  Probability of type I error is too high  Researcher is not “comfortable” stating that differences are real and not due to chance If the p-value <   reject the H 0  Probability of type I error is low enough  Researcher is “comfortable” stating that differences are real and not due to chance

42. Statistical vs. Practical Significance Distinction: 1. Statistical significance: There is an acceptably low chance of a type I error 2. Practical significance: The actual difference between the means are not trivial in their practical applications

43. Practically Significant? Knowledge and experience Examine effect size  The magnitude of the effect Examples of measures of effect size:  Eta-squared (  2 )  Cohen’s d R2R2 Interpretation of effect size:  0.0 – 0.2 = small effect  0.21 – 0.8 = moderate effect  > 0.8 = large effect Examine power of test

44. Statistical Power Statistical power: The probability that you will correctly reject a false H 0 Power = 1 –  where   = probability of type II error Example: Statistical power = 0.80 therefore:  80% chance of correctly rejecting a false H 0  20% of accepting a false H 0 (type II error)

45. Researcher Conclusion Accept H 0 Reject H 0 Reality About Test No real difference exists Correct Conclusion Type I error Real difference exists Type II error Correct Conclusion

46. Statistical Power What influences power? 1. Sample size: As n increases, power increases - Under researcher’s control 2. Alpha: As  increases,  decreases therefore power increases - Under researcher’s control (to an extent) 3. Effect size: As ES increases, power increases - Not under researcher’s control

47. Statistical Power How much power is desirable? General rule: Set  as 4*  Example:   = 0.05, therfore  = 4*0.05 = 0.20  Power = 1 –  = 1 – 0.20 = 0.80

48. Statistical Power What if you don’t have enough power?  More subjects What if you can’t recruit more subjects and you want to prevent not having enough power?  Estimate optimal sample size a priori  See statistician with following information: Alpha Desired power Knowledge about effect size  what constitutes a small, moderate or large effect size relative to your dependent variable

49. Statistical Power Examples: 1. Novice athlete improves vertical jump height by 2 inches after 8 weeks of training 2. Elite athlete improves vertical jump height by 2 inches after 8 weeks of training

50. Agenda Introduction Hypothesis Testing  General Process Errors in Hypothesis Testing One vs. Two Tailed Tests Instat Example

51. Instat Type data from sample into a column.  Label column appropriately. Choose “Manage” Choose “Column Properties” Choose “Name” Choose “Statistics”  Choose “Simple Models” Choose “Normal, One Sample” Layout Menu:  Choose “Single Data Column”

52. Instat Data Column Menu:  Choose variable of interest. Parameter Menu  Choose “Mean, Known Variance (z-interval)”  Enter known SD or variance value. Confidence Level:  90% = alpha 0.10  95% = alpha 0.05

53. Instat Check “Significance Test” box:  Check “Two-Sided” if using non-directional hypothesis.  Enter value from null hypothesis. What population value are you basing your sample comparison? Click OK. Interpret the p-value!!!

54. Agenda Introduction Hypothesis Testing  General Process Errors in Hypothesis Testing One vs. Two Tailed Tests Instat Example

55. Example (p 246) Researchers want to investigate the effect of prenatal alcohol on birth weight in rats  Independent variable?  Dependent variable? Assume:  µ = 18 g   = 4  n = 16   M =  /√n = 4/4 = 1  M = 15 g

56. Step 1: State hypotheses (directional or non-directional) H 0 : µ alcohol = 18 g H 1 : µ alcohol ≠ 18 g Step 2: Set criteria for decision making Alpha (  ) = 0.05 Step 3: Sample data and calculate statistic Z = M - µ /  M Z = 15 – 18 / 1 = -3.0

57. Step 4: Make decision Does Z-score fall inside or outside of the critical region? Accept or reject? Statistical Software: p-value = 0.02  Accept or reject? p-value = 0.15  Accept or reject?

58. Homework Problems: 3, 5, 6, 7, 8, 11, 21