Explain the concept of hypothesis testing intuitively using distribution curves for null and alternate hypotheses

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Accepted Answer

Hypothesis testing is a statistical method to determine if there is enough evidence to support or reject a claim.

Hypothesis testing involves formulating a null hypothesis and an alternative hypothesis.
The null hypothesis assumes that there is no significant difference or relationship between variables.
The alternative hypothesis suggests that there is a significant difference or relationship between variables.
Distribution curves represent the probability distribution of data under the null and alternative hypotheses.
The null hypothesis curve is centered around the expected value, while the alternative hypothesis curve may be shifted.
Hypothesis testing involves calculating test statistics, such as t-statistic or z-score, to determine the likelihood of observing the data under the null hypothesis.
If the test statistic falls within the critical region (tail of the distribution), the null hypothesis is rejected in favor of the alternative hypothesis.
Examples: testing if a new drug is effective (null: no difference, alternative: drug is effective), testing if a website change improves conversion rate (null: no difference, alternative: change improves conversion rate).

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