| analytical comparison | A statistical technique that can be applied (usually after obtaining a significant omnibus F-test) to locate the specific source of systematic variation in an experiment.
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| ANOVA | The analysis of variance, or ANOVA, is the most commonly used inferential test for examining a null hypothesis when comparing more than two means in a single-factor study, or in studies with more than one factor (i.e., independent variable). The ANOVA test is based on analyzing different sources of variation in an experiment.
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| Cohen’s f | A measure of effect size when there are more than two means that defines an effect relative to the degree of dispersal among group means. Based on Cohen’s guidelines, an f value of .10, .25, and .40, defines a small, medium, and large effect size, respectively.
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| eta squared | A measure of the strength of association (or effect size) based on the proportion of variance accounted for by the effect of the independent variable on the dependent variable.
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| F-ratio | In the analysis of variance, or ANOVA, the ratio of between group variation and within group or error variation.
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| level of significance | The probability when testing the null hypothesis that is used to indicate whether an outcome is statistically significant. Level of significance, or alpha, is equal to the probability of a Type I error.
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| null hypothesis | Assumption used as the first step in statistical inference whereby the independent variable is said to have had no effect.
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| omnibus F-test | The initial overall analysis based on ANOVA.
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| power | Probability in a statistical test that a false null hypothesis will be rejected; power is related to the level of significance selected, the size of the treatment effect, and the sample size.
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| repeated measures (within-subjects) t | An inferential test for comparing two means from the same group of subjects or from two groups of subjects “matched” on some measure related to the dependent variable.
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| sensitivity of an experiment | Refers to the likelihood in an experiment that the effect of an independent variable will be detected when that variable does, indeed, have an effect; sensitivity is increased to the extent that error variation is reduced (e.g., by holding variables constant rather than balancing them).
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| simple main effect | Effect of one independent variable at one level of a second independent variable in a complex design.
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| single-factor independent groups design | An experiment that involves independent groups with one independent variable.
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| statistically significant | When the probability of an obtained difference in an experiment is smaller than would be expected if error variation alone were assumed to be responsible for the difference, the difference is statistically significant.
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| t-test for independent groups | An inferential test for comparing two means from different groups of subjects.
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| Type l error | The probability of rejecting the null hypothesis when it is true, equal to the level of significance.
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| Type ll error | The probability of failing to reject the null hypothesis when it is false.
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