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. 

Statistical inference and random sampling

Sampling and point estimates

Repeated sampling, point estimates and inference

Sampling and bias

Visualizing samples

Options

Depends on the sample

Does not depend on sample

Inference and bias

Confidence intervals and sampling

Normal sampling distributions

Calculating confidence intervals

Drawing conclusions from samples

Inferential Statistics and Sampling

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.


Normality tests

Testing for normality

Distribution of errors

Fitting a normal distribution

Correlation tests

Testing for correlation

Autocorrelation

Explained variance

Parametric tests

Equal variance

Normality of groups

ANOVA

Non-parametric tests

Comparing rankings

Comparing medians

Hypothesis Testing Toolkit

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.


Effect size

Effect size for means

Effect size for correlations

Effect size for categorical variables

Multiple comparisons and corrections

Multiple comparisons problem

Bonferonni-Holm correction

Power of a test

What is power anyway?

Power for experimental design

Computing power and sample sizes

Effect Size

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.


Bootstrapping

Bootstrap confidence intervals

Bootstrapping vs. normality

Combining evidence from p-values

Fisher's method in SciPy

Inference using Fisher's method

Summarizing Fisher's method

Permutation tests

Permutation tests for correlations

Permutation tests and bootstrapping

Analyzing skewed data with a permutation test

Course wrap-up video

Simulation, Randomization, and Meta-Analysis

Median Rent

VC Investments

Bitcoin

University Rankings

Austin Salaries

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.

<h2>Truly Understand Hypothesis Tests</h2>
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.

<h2>Analyze a Broad Range of Scenarios</h2>
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.

<h2>Draw Solid Conclusions From Big Data</h2>
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.

Hypothesis Testing in Python

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

Foundations of Inference in Python

Estatística aplicada em Python

Assistant Professor of Mathematics

Descriptive statistics

Inference

Statistical inference process

Point estimates

Sampling

Let's practice!

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Inferential Statistics and Sampling - Statistical inference and random sampling

Statistical inference and random sampling

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