Python Data Analysis Bootcamp class 9 - 08 Null vs Alternative Hypothesis

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The null hypothesis and the alternative hypothesis are fundamental concepts in hypothesis testing, a key statistical procedure.

The null hypothesis, often denoted as H0, is a statement that represents a default assumption or a claim that there is no effect, no difference, or no relationship in the population. In other words, it is a hypothesis that we want to test or challenge. It typically suggests that any observed differences or effects are due to random chance or sampling variability.

The alternative hypothesis, often denoted as Ha or H1, is the statement that contradicts the null hypothesis. It represents what we are trying to demonstrate or provide evidence for. It asserts that there is a real effect, difference, or relationship in the population, and it is the hypothesis we aim to support with our data and statistical analysis.

In a hypothesis test, we collect sample data and use statistical methods to assess whether the evidence from the data supports the null hypothesis (indicating that there is no effect) or the alternative hypothesis (indicating that there is a real effect). The results of the test allow us to make informed decisions and draw conclusions about the population based on the evidence provided by the sample.

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