Foundations of Inference in Python

Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.

What happens after you compute your averages and make your graphs? How do you go from descriptive statistics to confident decision-making? How can you apply hypothesis tests to solve real-world problems? In this four-hour course on inference foundations in Python, you’ll get hands-on experience making sound conclusions based on data. You’ll learn all about sampling and discover how improper sampling can throw statistical inference off course.

You'll start by working with hypothesis tests for normality and correlation, as well as both parametric and non-parametric tests. You'll run these tests using SciPy, and interpret their output for decision-making.

Next, you'll measure the strength of an outcome using effect size and statistical power, all while avoiding spurious correlations by applying corrections.

Finally, you'll use simulation, randomization, and meta-analysis to work with a broad range of data, including re-analyzing results from other researchers.

Following the course, you will be able to successfully take big data and use it to make principled decisions that leaders can rely on. You'll go beyond graphs and summary statistics to produce reliable, repeatable, and explainable results.

• Inferential Statistics and Sampling
• In this chapter, we'll explore the relationship between samples and statistically justifiable conclusions. Choosing a sample is the basis of making sound statistical decisions, and we’ll explore how the choice of a sample affects the outcome of your inference.
• Hypothesis Testing Toolkit
• Learn all about applying normality tests, correlation tests, and parametric and non-parametric tests for sound inference. Hypothesis tests are tools, and choosing the right tool for the job is critical for statistical decision-making. While you may be familiar with some of these tests in introductory courses, you'll go deeper to enhance your inferential toolkit in this chapter.
  
• Effect Size
• In this chapter, you'll measure and interpret effect size in various situations, encounter the multiple comparisons problem, and explore the power of a test in depth. While p-values tell you if a significant effect is present, they don't tell you how strong that effect is. Effect size measures how strong an effect a treatment has. Master the factors underpinning effect size in this chapter.
  
• Simulation, Randomization, and Meta-Analysis
• You’ll expand your inferential statistics toolkit further with a look at bootstrapping, permutation tests, and methods of combining evidence from p-values. Bootstrapping will provide you with a first look at statistical simulation. In the lesson meta-analysis, you’ll learn all about combining results from multiple studies. You’ll end with a look at permutation tests, a powerful and flexible non-parametric statistical tool.