cogsci-statistics

Use a mixed-effects model with crossed random effects by including random intercepts and slopes for both subjects and items in your formula. This skill provides specific modeling recipes for LMM and GLMM structures to ensure rigorous statistical results.

Use a Gamma GLMM for reaction time data to handle right-skewed distributions, and apply cluster-based permutation tests for EEG analysis to address multiple comparisons. This skill guides the selection between these approaches based on your experimental data type.

The language-as-fixed-effect fallacy in cognitive science research occurs when experimental items are incorrectly treated as fixed effects instead of random effects, leading to inflated false positives. This skill addresses such pitfalls with rigorous modeling protocols.

Report effect sizes and statistical results in compliance with APA 7th edition by including standardized metrics, confidence intervals, and exact p-values alongside your mixed-effects model outputs. This skill ensures your reporting meets these rigorous standards.

Handle outlier exclusion and multiple comparison corrections by applying predefined criteria and using correction methods appropriate for your data structure to control false positives. This skill offers methodological guidance to ensure robust and reproducible results.

You can use Bayesian approaches for experimental data in cognitive science research as an alternative to traditional frequentist methods. This skill facilitates the selection and implementation of Bayesian approaches for robust statistical modeling.