1. Correct decisions –The null hypothesis is true and it is accepted –The null hypothesis is false and it is rejected Incorrect decisions –Type I Error The null hypothesis is true and it is rejected The probability of committing type-I error is denoted by α or p-value or sig. level in SPSS –Type II Error The null hypothesis is false and it is accepted The probability of committing type-II error is denoted by β 1 Type I and Type II Errors

2. True state Decision H o trueH o false Reject H o Type I error Correct decision Do not reject H o Correct decision Type II error 2

3. Nothing is proved absolutely. The only way to prove anything statistically is to use the entire population, which is in most cases not possible. Then the decision, is made on the basis of probabilities. That is when there is a large difference between the mean obtained from the sample, and the hypothesized mean, the null hypothesis is probably not true. The question is, How large a difference is necessary to reject the null hypothesis? Here is the level of significance is used. It is the maximum probability of committing a type I error, this probability is symbolized by α. Statistical analyses to help decide whether to accept or reject the null hypothesis Alpha level: Common levels used in social Sciences.01 There is a 1% chance of rejecting a true null hypothesis.05 There is a 5% chance of rejecting a true null hypothesis.10 There is a 10% chance of rejecting a true null hypothesis 3 Level of Significance

4. The smaller the p-value, the more statistical evidence exists to support the alternative hypothesis. If the p-value is less than 1%, there is overwhelming evidence that supports the alternative hypothesis. If the p-value is between 1% and 5%, there is a strong evidence that supports the alternative hypothesis. If the p-value is between 5% and 10% there is a weak evidence that supports the alternative hypothesis. If the p-value exceeds 10%, there is no evidence that supports the alternative hypothesis. 4 Interpreting the p-value…

5. 5 Overwhelming Evidence (Highly Significant) Strong Evidence (Significant) Weak Evidence (Not Significant) No Evidence (Not Significant) 0.01.05.10 p=.0069

6. One-tailed –A one-tailed test indicates that the null hypothesis should be rejected when the test value is in the critical region on one side of the mean. A one-tailed is either right-tailed or left-tailed, depending on the direction of the inequality of the alternative hypothesis –The hypothesis called the Directional hypothesis or Simple Hyp Average IQ score of Preston Uni. student is less than 130 Higher IQ score leads to higher academic achievement Two-tailed –In a two-tailed, the null hypothesis should be rejected when the test value is in either of the two critical regions – The hypothesis called the Non-directional hypothesis or Composite hypothesis Average IQ score of Preston Uni. student is not equal to 130 6 One-Tailed and Two-Tailed Tests Directional & non-directional

7. Calculate the critical value of the mean ( ) and compare against the observed value of the sample mean ( )… 7 Right-Tail Testing…

8. Calculate the critical value of the mean ( ) and compare against the observed value of the sample mean ( )… 8 Left-Tail Testing…

9. Two tail testing is used when we want to test a research hypothesis that a parameter is not equal (≠) to some value 9 Two–Tail Testing…

10. 10 Summary of One- and Two-Tail Tests… One-Tail Test (left tail) Two-Tail TestOne-Tail Test (right tail)

11. 1.State the null and alternative hypotheses 2.Identify the criteria for significance 3.Identify & run the appropriate test of statistics 4.Make the decision to reject or not reject the null hypothesis 5.Summarize the results according to APA style 11 Steps in Solving Hypothesis Testing

12. Identify the criteria for significance –If computing by hand, identify the critical value of the test statistic –If using SPSS-Windows, identify the probability level of the observed test statistic Compare the computed test statistic to the criteria for significance –If computing by hand, compare the observed test statistic to the critical value –If using SPSS-Windows, compare the probability level of the observed test statistic to the alpha level 12 Steps in Solving Hypothesis Testing

13. Accept or reject the null hypothesis –Accept The observed test statistic is smaller than the critical value The observed probability level of the observed statistic is greater than alpha –Reject The observed test statistic is larger than the critical value The observed probability level of the observed statistic is smaller than alpha 13 Steps in Solving Hypothesis Testing