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Data Science Detective

"I never guess. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts." Sherlock Holmes

Hypothesis testing is important in data science because it helps us make sense of the data and draw meaningful conclusions. It's like a detective's tool that allows us to investigate whether what we observe in the data is real or just a coincidence. By using hypothesis testing, we can make informed decisions and find answers to questions like 'Are these differences we see in the data significant?' or 'Do these two groups really differ from each other?' It helps us validate our ideas, compare things, and evaluate how well our models are performing. Essentially, hypothesis testing is a powerful way to bring clarity and confidence to our data analysis.

To see Hypothesis testing in action please take a look at my project using EPA data.

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