I have 4 independent groups (with different n) with values ranging from 0.0 to 1.0 and want to check if the mean of each group differs significantly (p<0.05) from a hypothetical value. Is it possible to do this with multiple t-tests and correct for the family-wise error rate with the Bonferroni method or the FDR approach?
For example following imaginary dataset of group I-IV containing, ideally gaussian distributed, mortalities. Hypothetical mean value to test against: 0.2
Data set:
Column statistics with t-test against a theoretical mean of 0.2 (p=0.05).
With Bonferroni correction the adjusted p-value would be 0.0125 (4 tests -> 0.05/4=0.0125), therefore the mean of group II does not differ significantly from 0.2 although its t-test p-value is smaller than 0.05.
Can it be done this way (or in a similar way with the FDR approach)?
For example following imaginary dataset of group I-IV containing, ideally gaussian distributed, mortalities. Hypothetical mean value to test against: 0.2
Data set:
Column statistics with t-test against a theoretical mean of 0.2 (p=0.05).
With Bonferroni correction the adjusted p-value would be 0.0125 (4 tests -> 0.05/4=0.0125), therefore the mean of group II does not differ significantly from 0.2 although its t-test p-value is smaller than 0.05.
Can it be done this way (or in a similar way with the FDR approach)?
