From beginner's guides to advanced tricks, find it all here.

How to Determine Causal Relationships in Observational Studies

Introduction In the previous blog post, we looked at controlled experiments and saw what techniques we can use to properly analyze them. Unfortunately though, we don’t always have the option to use a controlled experiment. There are times when data has already been collected and we still want to properly analyze it… …or other times when a controlled experiment is unfeasible or unethical (e.g. unregulated refusing of treatments for patients). Of course, there’s still a

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How to Evaluate A/B Tests (aka Controlled Experiments) for Causation and Correlation

In the previous blog post, we looked at the terms causation and correlation and developed a deeper understanding of what exactly each of those terms means and what the difference between causation and correlation is. In this post, we’re going to continue from where we left off last time and dive into how we can approach different types of datasets and go about analyzing them correctly. More specifically, in the next two blog posts, we’re

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Correlation vs Causation: What’s the Difference? (+ Examples!)

Once and for all – what are correlation and causation? How do you differentiate between correlation vs causation?

In this blog post, we discuss what correlations and causations are, some properties and types of correlations plus what noise is, and of course, you’ll find some examples to guide you along the way!

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What are Bar Graphs? & How to Use Them

Good ol’ bar graphs are some of the easiest and simplest forms of data visualizations, but let’s take it beyond what we learned in fourth-grade math and get into some of the more advanced questions when it comes to bar graphs, including when to use bar graphs, how to use them, and how to make them in Python.

Plus, let’s also talk through what the differences between bar graphs vs. histograms vs. box plots are and when to use which type of visualization.

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