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INTRODUCTION TO ANALYSIS OF VARIANCE (ANOVA): a hypothesis-testing…
INTRODUCTION TO ANALYSIS OF VARIANCE (ANOVA): a hypothesis-testing procedure that is used to evaluate mean differences between two or more treatments (or populations)
HOW DOES ANOVA DIFFER FROM t-TEST?:
The major advantage of ANOVA is that it can be used to compare two or more treatments, providing researchers with much greater flexibility in designing experiments and interpreting results.
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F-RATIO:
the test statistic for ANOVA; comparison between how much difference exists versus how big the differences are between treatment conditions
WHAT DOES F-RATIO DETERMINE?:
The value obtained for the F-ratio helps determine whether any treatment effects exist.
ERROR TERM:
measure of the variance caused by random, unsystematic differences
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DISTRIBUTION OF F-RATIOS:
all possible F values that can be obtained when the null hypothesis is true
CHARACTERISTICS OF DISTRIBUTION:
F values always are positive numbers AND the distribution of F-ratios should pile up around 1.00
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eta SQUARED:
percentage of variance accounted for by the treatment effect in published reports of ANOVA results
post-hoc TEST:
additional hypothesis test done after an ANOVA to determine whether mean differences are significant
TUKEY'S HONESTLY SIGNIFICANT DIFFERENCE (HSD) TEST: computation of single value that determines the minimum difference between treatment means necessary for significance
THE Scheffé TEST:
method using an F-ratio to evaluate the significance of the difference between two treatment conditions
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