Statistical methods

Need help?

  • Do not hesitate to ask Jeanette Mumford for help with any statistical analyses.

Multiple testing

  • Methods for multiple testing correction should be chosen prior to data analysis and reported in the preregistration document.

  • The nature of grouping for multiple testing corrections (e.g., parameter within single models, sets ot models, etc) should be pre-specified.

  • For fMRI data, if using cluster-based thresholding the cluster forming threshold must be Z ≥ 3.1 if using parametric thresholding. There are no restrictions on threshold when using nonparametric thresholding, but the threshold should be pre-specified.


  • Proper cross-validation practices should be considered. Refer to for more detail.

    • In-sample model fit indices should not be reported as evidence for predictive accuracy

    • The cross-validation procedure should encompass all operations applied to the data

    • Prediction analyses should not be performed with samples smaller than several hundred observations

    • Multiple measures of prediction accuracy should be examined and reported

    • The coefficient of determination should be computed using the sums of squares formulation and not the squared correlation coefficient

    • Leave-one-out cross-validation should be avoided in favor of shuffle-split or K-fold cross-validation.

Mixed-effects models

Model suitability and diagnostics

  • Does the model properly test the hypothesis of interest?

  • Do the data conform to the assumptions of the model?

  • All model fits should be criticized to identify violation of any assumptions or presence of outliers.

Reporting results

  • Effect size, parameter estimate, standard error, p-value and confidence interval should all be reported.

  • In cases where null effects are reported, some measure of evidence for the null hypothesis (e.g. Bayes factors, equivalence tests) should be reported.

  • Results for all analyses that were preregistered should be reported.