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# 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.

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4 Horas14 Videos48 Exercicios

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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.
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1. 1

### Inferential Statistics and Sampling

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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.

Reproduzir Capítulo Agora
Statistical inference and random sampling
50 xp
Sampling and point estimates
100 xp
Repeated sampling, point estimates and inference
100 xp
Sampling and bias
50 xp
Visualizing samples
100 xp
Inference and bias
100 xp
Confidence intervals and sampling
50 xp
Normal sampling distributions
100 xp
Calculating confidence intervals
100 xp
Drawing conclusions from samples
100 xp
2. 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. 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. 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.

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### Nas seguintes faixas

#### Applied Statistics in Python

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Paul Savala

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