In data science, we often face a question: “Is this change really working, or is it just random?” Hypothesis testing is a simple way to answer this using data and statistics.
What is Hypothesis Testing?
Imagine you own an online shop and change the checkout page design. Now, you want to know if sales have actually improved. Hypothesis testing helps check if this change is real or just by chance.
You start with two statements:
Null Hypothesis (H₀): No effect or difference
Alternative Hypothesis (H₁): There is an effect or difference
How It Works
State H₀ and H₁
Decide an error limit (usually 5%)
Choose the right statistical test – T-test, Z-test, Chi-square, or ANOVA
Collect clean and relevant data
Perform the test using tools like Python, R, or Excel
Compare the result with your error limit
Decide whether to accept or reject H₀
Where It’s Used
E-commerce: Testing product descriptions or designs
Education: Comparing test scores between two classes
Market Research: Checking survey relationships (e.g., gender vs buying preference)
Tips for Students
Match the correct test with your data type
Never rely only on numbers – think about the real-world meaning
Use it early in your analysis process
Hypothesis testing is like asking your data for proof before making a decision. In the tech world, it keeps your choices fact-based, not guess-based.
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