In our previous post, we explored One-Way ANOVA, which allows us to compare the means of multiple groups based on one independent variable (factor). But what if we want to analyze the impact of two factors simultaneously? This is where Two-Way ANOVA comes into play! It helps us determine:If each Read more…
When working with data, we often want to compare the means of multiple groups to see if there’s a statistically significant difference between them. But how do we do this? If we compare groups one by one using t-tests, we increase the risk of errors. This is where One-Way ANOVA Read more…
Hypothesis testing is a fundamental concept in statistics and data science. But making decisions based on data isn’t always straightforward – we can make errors in our conclusions. Understanding these errors and the role of significance levels, p-values, and statistical power helps us make better decisions with confidence. Let’s break Read more…
One of the biggest pitfalls in data science (and in life, really) is assuming that correlation implies causation. Just because two things move together doesn’t mean one causes the other. Let’s break it down with examples, common mistakes, and ways to avoid falling into this trap. Correlation: When Two Things Read more…
The Story of Survivorship Bias and Abraham Wald Have you ever looked at successful companies, athletes, or strategies and thought, “This must be the formula for success”? If so, you might be falling into survivorship bias – a classic mistake in data analysis. One of the best examples of this Read more…
When making decisions in business, marketing, or product development, intuition isn’t enough – you need data to back it up. That’s where A/B testing comes in! It’s one of the simplest and most powerful ways to compare two options and determine which one performs better. What is A/B Testing? A/B Read more…