Improving your statistical inferences course
https://www.coursera.org/learn/statistical-inferences
Course structure
Week 1 Introduction + Frequentist Statistics
- Interpret p-values correctly.
- Examine the distribution of p-values as a function of the statistical power of the test.
Week 2 Likelihoods & Bayesian Statistics
- Describe the differences between likelihood and Bayesian approaches
- Apply Likelihood ratios and Bayesian analyses in a simple binomial sampling situation
- Evaluate the strengths and weaknesses of likelihood and Bayesian approaches to inferences.
- Distinguish between Frequentist, Likelihood, and Bayesian approaches.
- Discuss the benefits of Bayesian thinking when drawing statistical inferences.
Week 3 Multiple Comparisons, Statistical Power, Pre-Registration
- Describe The difference between Type 1 and Type 2 errors.
- Explain which practices inflate Type 1 errors.
- Predict which outcomes are most likely in research you design.
- Recognize the effects of optional stopping.
- Demonstrate you know how to control error rates.
Week 4 Effect Sizes
- Compare standardized and unstandardized effect sizes.
- Compute effect sizes from summary data or test statistics.
- Interpret effect sizes.
Week 5 Confidence Intervals, Sample Size Justification, P-Curve Analysis
- Interpret confidence intervals correctly.
- Distinguish Frequentist confidence intervals and Bayesian credible intervals.
- Justify the sample size in your study.
- Apply p-curve analysis to evaluate the evidential value in sets of studies.
Week 6 Philosophy of Science & Theory
- Support the lack of an effect worthwhile to examine statistically.
- Describe different viewpoints on philosophy of science
- Recognize different ways to facilitate theory construction.
- Judge whether the null hypothesis is a valid prediction.
Week 7 Open Science
- Show you can pre-register your experiment.
- Show you can share the data and analysis scripts with your research report.