# Lecture 11 Psyc 300A. Null Hypothesis Testing Null hypothesis: the statistical hypothesis that there is no relationship between the variables you are.

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Lecture 11 Psyc 300A

Null Hypothesis Testing Null hypothesis: the statistical hypothesis that there is no relationship between the variables you are studying (e.g., no difference in means) The null says the differences that may be observed are due to chance. Research hypothesis: the hypothesis that there is a relationship between variables.

Directional vs Nondirectional Research Hypotheses Nondirectional: The direction of the difference between groups is not specified. __ __ H 1 : X 9 = X 12 Directional: The direction of the difference is specified. __ __ H 1 : X 12 > X 9

Inferential Statistics We test our sample recognizing that differences we observe may be simply due to chance. Significance level (alpha) is the risk we are willing to assume that we will say there is a relationship between variables when in fact there isn’t (it’s due to chance).

Type I and Type II Errors Accept the Null Hypothesis Reject the Null Hypothesis Null is really True Correct Decision Type I Error Null is really False Type II Error Correct Decision

How to Do Tests of Significance 1.State the null hypothesis 2.Set the alpha level (usually.05 or.01) 3.Decide on the appropriate test statistic 4.Compute the test statistic value (or have SPSS do it) 5.Determine the value needed for rejection of null hypothesis (critical value) 6.Compare the obtained to critical value 7.Decide to accept or reject null hypothesis

t-test Statistic used to compare two means (when there is one IV with 2 levels) Basic idea: Is the difference between two means large relative to the spread around each mean?

t-test

Obtain critical value from a table (p. 377 in your book) Need to know df One or two-tailed

t-test: An example Is in-home counseling better than out-of home counseling for elders with anxiety? In homeOut of home 3476 1178 1376 3656 5142 4554 4436 3675 7743 7887

t-test: SPSS Output

What affects t-test Difference in means (strong manipulation) Smaller variability (increased control) Bigger sample size

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