**Analysis of Testing‐Based Forward Model Selection**Kozbur, D. (2020). Analysis of testing‐based forward model selection.*Econometrica, 88*(5), 2147–2173.

**This resource analyzes a procedure called Testing‐Based Forward Model Selection (TBFMS) in linear regression problems.****Applying Linear and Nonlinear Models for the Estimation of Particulate Matter Variability**Tzanis, C. G., Alimissis, A., Philippopoulos, K., & Deligiorgi, D. (2019). Applying linear and nonlinear models for the estimation of particulate matter variability.*Environmental Pollution, 246*, 89–98.

**This resource uses Inverse Distance Weighting, two linear regression models, the Multiple Linear Regression, and the Linear Mixed Model, along with a Feed Forward Neural Network (FFNN) model methods for the purpose of evaluating various methodologies used for spatial interpolation in the context of proposing an effective yet simple to apply scheme for PM spatial point estimations.****Comparing Gaussian Graphical Models with the Posterior Predictive Distribution and Bayesian Model Selection**Williams, D. R., Rast, P., Pericchi, L. R., & Mulder, J. (2020). Comparing Gaussian graphical models with the posterior predictive distribution and Bayesian model selection.*Psychological Methods, 25*(5), 653–672.

**This resource uses simulation to show the posterior predictive method is approximately calibrated under the null hypothesis (α = .05) and has more power to detect differences than alternative approaches.****Hypothesis Testing**Majaski, C. (2020, October 24). Hypothesis testing.

**This resource defines hypothesis testing.****Model Selection Versus Traditional Hypothesis Testing in Circular Statistics: A Simulation Study**Landler, L., Ruxton, G. D., & E. Pascal Malkemper. (2020). Model selection versus traditional hypothesis testing in circular statistics: A simulation study.*Biology Open, 9*(6).

**This resource presents simulation data that demonstrate that the distribution model to alternative model-based inference can offer very similar performance to the best traditional tests, but only if an adjustment is made in order to control type I error rate.****Nonparametric Tests for Optimal Predictive Ability**Arvanitis, S., Post, T., Potì, V., & Karabati, S. (2021). Nonparametric tests for Optimal Predictive Ability.*International Journal of Forecasting, 37*(2), 881–898.

**This resource highlights the development and implementation of a nonparametric method for comparing multiple forecast models.****Simply Psychology – What is a Hypotheses?**McLeod, S. (2018, August 10). Simply psychology – What is a hypotheses?

**This resource defines hypotheses and types of hypotheses.****Statistical Tests: Which One Should You Use?**Bevans, R. (2020, December 28). Statistical tests: Which one should you use?

**This resource gives directions on selecting the right statistical tests.**

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