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1 Hypothesis Testing William P. Wattles, Ph.D. Psychology 302.

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Presentation on theme: "1 Hypothesis Testing William P. Wattles, Ph.D. Psychology 302."— Presentation transcript:

1 1 Hypothesis Testing William P. Wattles, Ph.D. Psychology 302

2 2 Statistical Inference F Provides methods for drawing conclusions about a population from sample data. Sample (statistic) Population (parameter)

3 3 The problem F Sampling Error

4 4 Sampling error results from chance factors that produce a sample statistic different from the population parameter it represents.

5 5 Dealing with sampling error F Confidence intervals F Hypothesis testing

6 6 Hypothesis testing F We use confidence intervals when our goal is to estimate a population parameter.

7 7

8 8 Hypothesis testing F A more common need is to assess the evidence for some claim about the population.

9 9 Tests of significance F Does a change in the independent variable produce a change in the dependent variable. F Or is the observed difference merely the result of sampling error? F Is the observed difference meaningful (significant).

10 10 Hypothesis Testing F Dr. Diligent has found a better treatment for procrastination. She reports that students trained in her method have a higher than the average. FMU

11 11 Null, says: “It’s nothing but sampling error.

12 12 Dr. Diligent offers an alternative hypothesis that the difference probably did not come about by chance. If she is correct the observed effect would be unlikely to occur by chance.

13 13 Dr. Diligent says that the sample comes from a different population with a different mean. FMU

14 14 Dr. Diligent says that the sample comes from a different population with a different mean. pop mean 72.55 pop std dev 12.62 sample mean 79.53 n=25 FMU

15 15 Who is correct? F Ha F Ho FMU

16 16 Hypothesis test  μ =72.55, σ =12.62 F n=25, M=79.53 F std err=std dev/sqrt N F Std err=12/5=2.52  Z=M- μ / σ M F Z=79.53-72.55/2.52=+2.77 F Area beyond +2.77.0028

17 17 F μ population mean F σ population std dev F M sample mean F n sample size F σ M Standard error of the mean.  Z obt Z score of the sample mean Z obt = M-μ/σ M

18 18 Statistical Significance F 2.77 > 1.96 F p <.05

19 19 Reject the null hypothesis F The results probably did not occur by chance. F There must be something to her procrastination training program.

20 20 Null hypothesis F null hypothesis (Ho) states that there is no difference between the population means. Any observed difference is random sampling error. F alternative hypothesis states that the means are different.

21 21 Statistical significance F Means we have concluded that the data are too unlikely to have occurred by chance alone. Thus, there is a relationship between the independent and dependent variable. F Means we have rejected the null hypothesis Ho.

22 22 Statistical significance F Failure to reject Ho suggests that the difference could have occurred by chance and we conclude that the means are the same.

23 23 P-Value F The probability of obtaining a value as extreme or more extreme than the observed statistic. F The probability that the test would produce a result at least as extreme as the observed result if the null hypothesis were true.

24 24 Alpha or Significance level F Statistical significance simply means rareness. F Another term for significance level is alpha level. F.05 is generally considered the minimum necessary for significance.

25 25 Statistically significant F We can calculate a P-value using the area under the curve. It tells us how likely the obtained statistic would be if the null hypothesis were true.  Level of significance alpha  says how much evidence we require. F Usually.05,.01 or.001

26 26 Statistically significant F If the P-value is as small or smaller than alpha, we say that the data are statistically significant at level alpha.

27 27 Critical Z F The Z score that cuts off the most extreme 5% of the scores. F One tail versus two tail. F Two tail – 1.965% – 2.5761% F One Tail – 1.6455% – 2.3261%

28 28 Two-tail test F Divides the critical region into two areas, each cutting off half the alpha level.

29 29 One-tail test F A one-tailed significance test has only one critical regions and one critical value. Not frequently used.

30 30 One-tail vs.. two-tail F One tail used if problem specifies a direction. (I.e., is greater than, taller than) F Two tail used when the alternative hypothesis is that the two means are different. F A one-tail test is more powerful

31 31 Power F the probability of rejecting a false null hypothesis.

32 32 Hypothesis test example F Job satisfaction scores at a factory have a standard deviation of 60. F Example 14.8 page 375 F X = self-paced- machine paced

33 33 Hypothesis test  μ =0, σ =60, M=17,n=18  Z=M- μ / σ M Fstd err=std dev/sqrt N FStd err=60/sqrt18=14.14 F Z=17-0/14.14 = 1.20 F P-Value 1.20 =.1151 * 2=.2302

34 34

35 35 F P value=.23 which is greater than.05 F Fail to reject the null hypothesis

36 36 P-values F The probability of a score as extreme as the observed score. F The decisive value of P is called the significance level. F Signified by the Greek letter alpha F Most commonly is.05

37 37 14.20 Reading a computer screen F Do these data give evidence that it takes longer to read with Gigi font?

38 38 14.20 Reading a computer screen F 25 adults F Pop std dev = 6 seconds F Mean time for Times New Roman is 22 seconds

39 39 14.20 Reading a computer screen F Do these data give evidence that it takes longer to read with Gigi font?

40 40 14.20 Reading a computer screen

41 41 14.55 page 390 F Does eye grease increase sensitivity?  Ho= μ = 0  Ha μ > 0

42 42 14.55 page 390 F P is less than.05 F Reject null hypothesis F Accept alternative hypothesis F Data suggest that grease increases sensitivity

43 43 Inference as a decision F We make a decision to accept Ho or Ha. F Sometimes we are correct F Sometimes we are wrong.

44 44 Type I and Type II errors

45 45 Type I error F If we reject Ho when in fact Ho is true F If we decide it was not chance when in fact it was chance.

46 46 Type II error F If we accept Ho when Ho is false. F If we attribute a result to chance when it is not chance.

47 47 Effect Size F Hypothesis testing looks at the statistical significance of the effect F Effect size looks at the size of the effect. F Different procedures use different measures of effect size.

48 48 Cohen’s d F The number of standard deviations an effect shifted above or below the mean stated in the null hypothesis.

49 49 Cohen’s d F Cohen’s d equals zero when the means are the same and rises as they differ.

50 50 The End

51 51 Hypothesis test for music trivia data

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