Reasoning of conditions for testing

Can you explain the importance of the conditions for hypothesis testing, like why is it important for the data to be less than 10 percent of the population?

Check the 10% condition when taking a random sample without replacement. The 10% condition allows you to establish independence.

Conditions are used to check if the test meets the conditions for a normal model to be used. The less than 10 percent condition is used to make sure that proper sampling is possible. You wouldn’t want your sample population to be more than 10% of the overall population since that would not allow different parts of the population to be randomly selected.

The other conditions, such as success/failure (np and n(1-p) ), randomization, Central Limit Theorem, and the previous one are all supportive of making sure your normal model can be used. They allow us to be sure the data is not skewed and can fit into the standards for a Normal Model.

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