Hypothesis testing is a statistical procedure that allows us to test assumptions or beliefs about a population based on sample data. It is a statistical procedure that is used to determine whether a hypothesis about a population parameter is supported by the evidence in a sample. It helps us determine the likelihood that our assumptions are true, given the collected data.

In hypothesis testing, the researcher first specifies a null hypothesis and an alternative hypothesis. The null hypothesis represents the default assumption that there is no significant difference or relationship between the variables being studied. The alternative hypothesis represents the claim or hypothesis that the researcher is testing.

Next, the researcher collects a sample of data and uses statistical tests to determine whether the sample data provide sufficient evidence to reject the null hypothesis in favour of the alternative hypothesis. The null hypothesis is retained if the sample data does not support the alternative hypothesis. If the sample data does support the alternative hypothesis, the null hypothesis is rejected.

The outcome of hypothesis tests comes in two forms:

- Reject the null hypothesis, and
- Fail to reject the null hypothesis

The outcome of a hypothesis test is typically expressed in terms of a p-value, which represents the probability of obtaining the observed results by chance if the null hypothesis is true.

- If the p-value is below a predetermined threshold (usually 0.05), the
**null hypothesis is rejected**, and the alternative hypothesis is accepted.**(If the p is low, null must go)** - If the p-value is above the threshold, we
**fail to reject**the null hypothesis, and the null hypothesis is retained**(if the p is high, the null fly)**.

Hypothesis testing is a commonly used statistical method for testing claims and hypotheses about a population based on sample data. It is a valuable tool for making inferences about a population based on the evidence in the sample data.