Hypothesis testing is a statistical method used to make informed decisions or draw conclusions about a population based on sample data. It involves several key concepts, including null and alternate hypotheses, significance level, test statistics, p-value, type 1 error, type 2 error, and power. Let's break down these concepts using the example of Facebook launching "reactions" as a new feature.

**Example: Facebook Launching Reactions**

**Null Hypothesis (H0):** The null hypothesis is a statement that there is no significant effect, no difference, or no change in the population parameter. In our Facebook example, the null hypothesis might be: "The introduction of 'reactions' does not significantly increase user engagement on the platform."

**Alternate Hypothesis (Ha):** The alternate hypothesis is the statement we want to test. It suggests that there is a significant effect, difference, or change in the population parameter. In this case, it could be: "The introduction of 'reactions' significantly increases user engagement on the platform."

**Significance Level (α):** The significance level, denoted by α (alpha), is the probability of making a Type 1 error (false positive). Commonly used significance levels are 0.05 or 5%. This represents the threshold for how much evidence we require to reject the null hypothesis.

**Test Statistic:** The test statistic is a value calculated from sample data that helps us make a decision about the null hypothesis. The specific test statistic depends on the type of test being conducted. For example, if we're comparing means, we might use the t-test or z-test.

**P-Value:** The p-value is the probability of observing a test statistic as extreme as, or more extreme than, the one calculated from the sample data, assuming the null hypothesis is true. A small p-value (typically less than α) suggests strong evidence against the null hypothesis. In our Facebook example, a small p-value would indicate that 'reactions' have a significant impact on user engagement.

**Type 1 Error (α):** A Type 1 error occurs when we reject the null hypothesis when it is actually true. In our example, this would mean concluding that 'reactions' significantly increase user engagement when they don't.

**Type 2 Error (β):** A Type 2 error occurs when we fail to reject the null hypothesis when it is actually false. In our example, this would mean failing to conclude that 'reactions' significantly increase user engagement when they do.

**Power:** Power (1 - β) is the probability of correctly rejecting the null hypothesis when it is false. In other words, it measures the ability of a test to detect a real effect. High power is desirable because it minimizes the chances of a Type 2 error.

In our Facebook example, suppose we collect data on user engagement before and after the introduction of 'reactions' and perform a statistical test. If the p-value is less than our chosen significance level (e.g., 0.05), we would reject the null hypothesis in favor of the alternate hypothesis, concluding that 'reactions' significantly increase user engagement. If the p-value is greater than 0.05, we would fail to reject the null hypothesis.

It's important to choose an appropriate significance level, understand the trade-off between Type 1 and Type 2 errors, and consider the power of the test when conducting hypothesis testing to make informed decisions based on data.