Always 2 Hypothesis
Yes, in a chi-square test, you always have two hypotheses:
1. Null Hypothesis (H₀):
- This is the default assumption: "There is no relationship or difference."
- In practical terms, it means what you observe is just random chance, and the data fits your expectations.
- Example:
- For candies, H₀ could be: "The number of red and blue candies picked is the same as expected."
2. Alternative Hypothesis (H₁):
- This is what you test against the null hypothesis: "There is a relationship or difference."
- If the observed data is significantly different from the expected, you reject the null hypothesis in favor of the alternative.
- Example:
- For candies, H₁ could be: "The number of red and blue candies picked is different from what I expected."
Why Do You Need Both?
Having two hypotheses ensures you're testing something specific:
- You start with the assumption (H₀) that there’s no effect or relationship.
- The data tells you whether to reject H₀ (and support H₁) or to stick with H₀ (because you didn’t find enough evidence).
Important Note:
Even if you reject the null hypothesis, it doesn’t prove the alternative hypothesis is true; it just shows the observed data is unlikely under H₀. In statistics, you're always testing for evidence, not proving absolute truth.
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