What are the best practices for statistical modeling and inference in programming?

2 Apply the right methods

Once you have chosen the right tools, you will need to apply the right methods for statistical modeling and inference. These methods include techniques, algorithms, and procedures that help you create, fit, evaluate, and compare statistical models that represent your data and your hypotheses. Some of the most common and useful methods for statistical modeling and inference in programming are linear and logistic regression, ANOVA and ANCOVA, t-tests and chi-square tests, correlation and causation analysis, cluster analysis and factor analysis, and machine learning and deep learning. You should understand the logic, assumptions, and requirements of each method, and apply the one that best matches your data and your research question.

4 Validate and interpret your models

After you run your statistical models and draw your inferences, you will need to validate and interpret your models. These steps involve assessing the quality, accuracy, and significance of your models and your inferences, and explaining what they mean in the context of your data and your research question. Some of the most relevant and helpful measures and indicators for validating and interpreting your models and your inferences are R-squared, adjusted R-squared, p-values, confidence intervals, effect sizes, coefficients, odds ratios, ROC curves, AUC, precision, recall, and F1-score. You should use these measures and indicators to evaluate your models and your inferences, and to report your findings in a clear and concise way.

5 Communicate your findings

The final step of statistical modeling and inference in programming is to communicate your findings. This step involves presenting and sharing your results, conclusions, and recommendations with your audience, whether it is your colleagues, clients, or the public. Some of the most effective and engaging ways to communicate your findings are graphs, charts, tables, dashboards, reports, slides, blogs, and podcasts. You should use these methods to visualize, summarize, and highlight your findings, and to tell a compelling and convincing story that answers your research question and supports your hypotheses.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?