A larger alpha level requires less difference between the means. You can then use a t-test table, found in most statistics books, to determine the "critical value" of t. If you obtained your subjects from multiple locations and assigned each person to be in one group or the other randomly, say through the use of a random numbers table, then you would use the t-test for independent samples in your analysis.

An example would be comparing math achievement scores of an experimental group with a control group.

Obviously, the ratings for this sample are correlated, because they came from the same individual. The distribution of the mean of your sample is normal. For this analysis, you would use the t-test for independent means. If the difference we find remains constant as we collect more and more data, we become more confident that we can trust the difference we are finding.

Note: The F-Max test can be substituted for the Levene test. Is a directional one-tailed or non-directional two-tailed hypothesis being tested? With studies involving group differences, effect size is the difference of the two means divided by the standard deviation of the control group or the average standard deviation of both groups if you do not have a control group.

H0: Males do not score significantly higher than females with respect to math achievement. A PowerPoint presentation on t tests has been created for your use. If our t test produces a t-value that results in a probability of.

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