Statistical power or 1 - β is therefore the probablity that we will correctly reject the null hypothesis. This is also known as the false negative rate or the Type II error rate. Statistical Power (1 - β): β is the probability that we will fail to reject the null hypothesis when the samples are drawn from different populations. With an α of 0.05, we would reject the null hypothesis when observing a difference that we would expect to see 5% (or less) of the time when drawing two samples from the same population. α can also be thought of as a measure of how extreme the observed difference in sample means has to be before we reject the null hypothesis. An α of 0.05 (5%) means that if we repeated an experiment where we drew samples from the same population many times, we would expect to incorrectly reject the null hypothesis in 5% of cases. Significance Level (α): The probability of incorrectly rejecting the null hypothesis (H 0: θ = 0 where θ = μ 1 - μ 2), also known as the false positive rate or the Type I error rate. This assumption holds if the underlying data are normally distributed, but not neccessarily if you are relying on the Central Limit Theorem for normally distributed sample means. With larger samples, the Central Limit Theorem typically means the sample means will be normally distributed. This does not require your underlying data to be normally distributed. This calculator does not require the groups to have equal variance as it uses the Welch's unequal variances t-test formulation by default 3. ![]() The sample means and sample variances are statistically independent.The sample variances ( s 2 1 and s 2 2) are χ 2 distributed.The sample means ( X 1 and X 2) are normally distributed. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |