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18 Important Statistical Approaches Every Data Scientist Should Know
Make Better Data-Driven Decisions with These Key Statistical Approaches
Statistics isn’t just a tool for data scientists — it’s the foundation of every model, every decision, and every discovery. In a world overflowing with data, the ability to apply the right statistical approach can mean the difference between a groundbreaking insight and a missed opportunity. In this article, we’ll explore 18 essential statistical techniques — from Bayesian inference to Survival Analysis — that every data scientist must understand and master to navigate real-world data challenges with confidence.
1. Hypothesis Testing (t-test)
Hypothesis testing is a statistical approach used to draw conclusions from experimental data. The t-test, in particular, is used to assess whether the difference between the means of two groups is statistically significant. This involves setting up a null hypothesis (H₀), which assumes no difference, and an alternative hypothesis (H₁), which suggests a meaningful difference exists.
Example Use Case
Testing if a new website design leads to higher user engagement compared to the old design.
