Learn Stats for Python IV: Statistical Inference

In today’s world, pervaded by data and AI-driven technologies and solutions, mastering their foundations is a guaranteed gateway to unlocking powerful insights from data and making effective and reliable data-driven decisions. One such family of foundational notions comes from nowhere other than statistics. Given its versatility and capabilities, as well as its popularity in data analysis and AI applications, learning stats with the aid of the Python programming language is an ideal approach to learning statistical concepts and putting them in practice: all at the same time!

Statology offers a wide range of Python-based stats tutorials that cover virtually every area and topic in statistics you can imagine—from descriptive statistics and data visualization to probability, statistical inference, predictive analysis, and more.

Not sure where to start, amid so many resources available? Don’t fret, here is the solution:

This tutorial series, consisting of five parts, curates and links together these “learn stats for Python” tutorials, providing you with a strong foundational learning pathway in both programming and statistics. Each tutorial is designed to be short, straight to the point, and easy to digest.

About Part IV: Statistical Inference

Part IV of this series fully delves into statistical inference: the process of analyzing data to infer properties -typically parameters like mean and variance- of an underlying probability distribution. Its methods infer properties of a population based on a sample of that population, for example by testing hypotheses and computing estimates.

1. Confidence intervals

Confidence intervals are range-based estimations of the true value of a population parameter like its mean, indicating that with a certain level of confidence, the true value of that parameter may lie somewhere within that interval. They provide insight into the uncertainty associated with a sample statistic. The following tutorials introduce methods for calculating and visualizing confidence intervals, which are a fundamental part of the hypothesis testing methods we will cover afterward.

2. Normality tests

Before conducting other statistical analysis, it is usually important to ensure that our data meets commonly desirable assumptions such as normality, in other words, the data can be modeled by using a normal distribution with a certain mean and standard deviation. Normality tests are designed to check this assumption and validate your data before moving on to more advanced analyses. These tutorials provide tools to help assess whether your data can be well-modeled by a normal probability distribution.

3. Comparison tests

These methods have the goal of testing for differences among two (or sometimes more) groups of data. Specifically, they test whether there are significant differences between the group means or variances, constituting a powerful tool to analyze the relationship between different datasets or data samples. Each test illustrated in the tutorials below considers specific nuances about the data, for instance, whether the groups are independent or related, whether they can accommodate testing for two groups versus more than two, etc. Some of these tests are nonparametric, which means they do not require assumptions of normality nor follow other specific probability distributions in the data at hand.

4. Variance analysis: ANOVA tests

Given their significance, ANOVA tests deserve their section in this post. ANOVA methods not only compare the means across different data groups but also help understand the intra-group and inter-group variability of the data. These four tutorials will help you acquaint with ANOVA tests in their multiple flavors.

Coming Up Next

The post ends here having explored a very representative sample of the methods for statistical inference available: there are a lot more, and you can find a more complete list here. The next post wraps up this tutorial series by highlighting tutorials focused on predictive analysis applications of statistics, namely time series analysis and regression.