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Foundations of Inference in Python
AdvancedSkill Level
Updated 12/2025Start Course for Free
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PythonProbability & Statistics4 hr14 videos48 Exercises4,050 XP3,486Statement of Accomplishment
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Truly Understand Hypothesis Tests
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 the foundations of inference in Python, you’ll get hands-on experience in making sound conclusions based on data. You’ll learn all about sampling and discover how improper sampling can throw statistical inference off course.Analyze a Broad Range of Scenarios
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 to use 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.Draw Solid Conclusions From Big Data
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 well beyond graphs and summary statistics to produce reliable, repeatable, and explainable results.Prerequisites
Hypothesis Testing in Python1
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.
2
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.
3
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.
4
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.
Foundations of Inference in Python
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