Assumptions of parametric data
Normally distributed data
Homogeneity of variance (variance should be the same throughout the data)
Interval data (at least)
Independence
—> repeated measure design: we expect scores in the experimental conditions to be non-independent for a given participant, but behavior between different participants should be independent
The assumption of normality
—> Central limit theorem => if the sample data are approximately normal then the sampling distribution should also be (N > 30)
As samples get larger (N > 30), the sampling distribution has a normal distribution with a mean equal to the population mean and a standard deviation
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