Improving your statistical inferences course

https://www.coursera.org/learn/statistical-inferences

---

• Lectures

  • Slides
  • Notes

• Assignments

---

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.