Bayesian inference is one proposed alternative to significance testing. (Nickerson cited 10 sources suggesting it, including Rozeboom (1960)). For example, Bayesian parameter estimation can provide rich information about the data from which researchers can draw inferences, while using uncertain priors that exert only minimal influence on the results when enough data is available. Psychologist John K. Kruschke has suggested Bayesian estimation as an alternative for the t-test and has also contrasted Bayesian estimation for assessing null values with Bayesian model comparison for hypothesis testing. Two competing models/hypotheses can be compared using Bayes factors. Bayesian methods could be criticized for requiring information that is seldom available in the cases where significance testing is most heavily used. Neither the prior probabilities nor the probability distribution of the test statistic under the alternative hypothesis are often available in the social sciences.
Bayesian inference is one proposed alternative to significance testing (Nickerson cited 10 sources suggesting it including Rozeboom (1960)) For example Bayesian parameter estimation can provide rich information about the data from which researchers can draw inferences while using uncertain priors that exert only minimal influence on the results when enough data is available Psychologist John K Kruschke has suggested Bayesian estimation as an alternative for the t-test and has also contrasted Bayesian estimation for assessing null values with Bayesian model comparison for hypothesis testing Two competing models/hypotheses can be compared using Bayes factors Bayesian methods could be criticized for requiring information that is seldom available in the cases where significance testing is most heavily used Neither the prior probabilities nor the probability distribution of the test statistic under the alternative hypothesis are often available in the social sciences