Understanding Type-I and Type-II Errors in Hypothesis Testing

Author(s): Deepak Chopra | Talking Data Science

Originally published on Towards AI.

An under-the-hood look into the concepts that underpin the all-important statistical hypothesis testing.

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Knowingly or unknowingly — we all hypothesize things daily!—We all can relate to thinking about whether route A will take less time than route B, if the average return on investment X is more than investment Y, and if movie ABC is better than movie XYZ. In all these cases, we are testing some hypotheses we have in our minds.

Setting up hypotheses, proving/disproving them using data, and helping businesses make decisions is like bread and butter for Data Scientists. Data Scientists often rely on probabilities to understand… Read the full blog for free on Medium.

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Published via Towards AI