"Type I" and "Type II" errors, names first given by Jerzy Neyman and Egon Pearson to describe rejecting a null hypothesis when it's true and accepting one when it's not, are too vague for stat newc...

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This lesson will give the definition of a null hypothesis, as well as an alternative hypothesis. Examples will be given to clearly illustrate the...

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If all else fails, use "significant at a p>0.05 level" and hope no one notices. // This concept totally went over my head in my research stats class.

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Videos from Dr. John Kruschke on the need for Bayesian Analysis. Rejecting the null hypothesis is not enough because a good p-value can be present with a huge confidence interval. Why some journals no longer want researchers to report p-values. Bayesian analysis is an improvement.

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