Strawberry or Mango? Let Data Decide and Understand Hypothesis Testing!
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Imagine your company launched two juice flavors: strawberry and mango. After a week, mango seems more popular. But is it a real winner, or just a lucky fluke?
A hypothesis test helps us find out. It checks whether the difference we see is real or just random. We start by assuming there’s no real difference between the flavors (this is the null hypothesis). Then, we test the opposite idea: that there is a real difference (alternative hypothesis).
The p-value is the key. It tells us the probability of seeing our results if there were truly no difference.
A small p-value (typically < 0.05) means our result is unlikely to be due to chance. For example, a p-value of 0.035 means there's only a 3.5% chance of seeing this difference if the flavors were actually the same. Since this is below 0.05, the result is statistically significant, so we have enough evidence to confidently say that mango really is preferred.
For the business, this matters. Hypothesis testing helps you make decisions based on evidence, not guesswork. It means you don’t just think mango is the winner—you can show why and invest in it confidently.