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These assumptions ensure that our sampling distribution is normal when working with sample means. That way, we can use z-scores / t-scores!
#1: Sample is randomly selected from the population.
#2: The sample size (n) is less than or equal to 10% of the population size (N)
#3: The sample size is greater than or equal to 30, or the population itself is normally distributed.
- Once sample size reaches 30, the Central Limit Theorem ensures that the sampling distribution will be normal.
- If the underlying population distribution is normal, then our sampling distribution will automatically be normal!
