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Understanding and Calculating Power after Two-Way ANOVA using SPSS

SPSS

power

statistical power

observed power

two-way ANOVA

two-factor ANOVA

factorial ANOVA

sample size

significance

effect size

population effect size

sample effect size

p-value

error

Type I

Type II

Type I error

Type II error

alpha

beta

alpha error

beta error

null hypothesis

rejection

fail to reject

true positive

true negative

false positive

false negative

interaction effect

main effect

independent variable

dependent variable

statistics

Grande

Автор: Dr. Todd Grande

Загружено: 2017-09-06

Просмотров: 36269

Описание: This video demonstrates how to understand and calculate statistical power after a two-way ANOVA using SPSS. Statistical power is the percentage chance that the null hypothesis will be rejected, when in reality, the null hypothesis is false. Statistical power is partially based on the effect size of the population, and in this example only the effect size from the sample is available. Caution should be used when interpreting “observed power” based on a sample effect size. The concepts of true positive, true negative, false positive, and false negative are reviewed. The relationship between the probability of a type II error (beta error) and power is reviewed. Alpha error (type I error) is discussed.

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Understanding and Calculating Power after Two-Way ANOVA using SPSS

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