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Summary Significance and Power Notes
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A document that allows you to have all notes in one place for the significance and power lecture.
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Significance and PowerPart one
Null hypothesis significance testing
Rules of significance testing
o 1) Assume the null hyp is true
o 2) Fit a statistical model to the data (this represents the alternative
hypothesis) – how well can the variants in our data be explained
o
o Systematic variance e.g., relationship bet two variables
o Unsystematic variance e.g. a variance in data set that can’t be explained by
the relationship e.g. maybe explained by extraneous variables
o 3) Calculate probability of getting that model or test statistic assuming that
null hyp is still true (aka our p value)
o If p<.05, our model fits the data well and we can gain confidence in the
alternative hypothesis
o But this isn’t the whole story as we only have a probability we need to figure
out how important the p value is and what sort of factors can affect this
o How important the p value is can be established by calculating levels of
power
Part two
What affects power?
3 mian factors
o Effect size
o No of ppts in a study
o Size of our alpha level
o Other factors – variability, design, test choice, tails of a test
o Knowing these 3 factors were able to figure out the fourth one
Effect size
o An objective and standardised measure of the magnitude of an effect
o Larger value = bigger effect size
o Depends on test conducted
Cohen’s d (for t tests)
Pearson’s r (correlation)
Partial eta squared (ANOVA)
Number of participants
o In conjunction and dependent on effect size
o Larger effect size = fewer ppts needed to get a ‘real’ effect
o Smaller effect size = more ppts needed to detect a ‘real’ effect
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