# Bayesian Independent - Sample Inference

This feature requires SPSS® Statistics Standard Edition or the Advanced Statistics option.

The Bayesian Independent - Sample Inference procedure provides options for using a group variable to define two unrelated groups, and make Bayesian inference on the difference of the two group means. You can estimate the Bayes factors by using different approaches, and also characterize the desired posterior distribution either assuming the variances are known or unknown.

- From the menus choose:
- Select the appropriate Test Variables from the source variables list. At least one source variable must be selected.
- Select the appropriate Grouping Variable from the source variables list.
A grouping variable defines two groups for the unpaired
*t*-test. The selected grouping variable can be either a numeric or a string variable. - Click Define Groups to define two groups for the
*t*test by specifying two values (for string variables), or two values, a midpoint, or a cut point (for numeric variables). - Select the desired Bayesian Analysis:
- Characterize Posterior Distribution: When selected, the Bayesian inference is made from a perspective that is approached by characterizing posterior distributions. You can investigate the marginal posterior distribution of the parameter(s) of interest by integrating out the other nuisance parameters, and further construct Bayesian confidence intervals to draw direct inference. This is the default setting.
- Estimate Bayes Factor: When selected, estimating Bayes factors (one of
the notable methodologies in Bayesian inference) constitutes a natural ratio to compare the marginal
likelihoods between a null and an alternative hypothesis.
Table 1. Commonly used thresholds to define significance of evidence Bayes Factor Evidence Category Bayes Factor Evidence Category Bayes Factor Evidence Category >100 Extreme Evidence for H0 1-3 Anecdotal Evidence for H0 1/30-1/10 Strong Evidence for H1 30-100 Very Strong Evidence for H0 1 No Evidence 1/100-1/30 Very Strong Evidence for H1 10-30 Strong Evidence for H0 1/3-1 Anecdotal Evidence for H1 1/100 Extreme Evidence for H1 3-10 Moderate Evidence for H0 1/10-1/3 Moderate Evidence for H1 *H0: Null Hypothesis**H1: Alternative Hypothesis* - Use Both Methods: When selected, both the Characterize Posterior Distribution and Estimate Bayes Factor inference methods as used.

- You can optionally click Criteria to specify Bayesian Independent-Sample Inference: Criteria settings (credible interval percentage, missing values options, and adaptive quadrature method settings), click Priors to specify Bayesian Independent-Sample Inference: Prior Distribution settings (data variance, prior on variance, and prior on mean conditional on variance), or click Bayes Factor to specify Bayesian Independent - Sample Inference: Bayes Factor settings.

^{1}Lee, M.D., and Wagenmakers, E.-J. 2013.

*Bayesian Modeling for Cognitive Science: A Practical Course*. Cambridge University Press.

^{2}Jeffreys, H. 1961.

*Theory of probability*. Oxford University Press.