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Explain the purpose of the Large Counts Condition.

To ensure the sampling distribution of the sample proportion is approximately normal, allowing for inference procedures.

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Explain the purpose of the Large Counts Condition.

To ensure the sampling distribution of the sample proportion is approximately normal, allowing for inference procedures.

Explain what a sampling distribution for the sample proportion represents.

It represents the distribution of possible values for the sample proportion if the study were repeated many times.

Why is the sampling distribution of the sample proportion useful?

It allows us to make inferences about the population proportion based on the sample data.

Define sample proportion ( $hat{p}$ ).

The fraction of successes in a sample; best guess for the true population proportion ( $p$ ).

What is the Large Counts Condition?

The condition where $np geq 10$ and $n(1-p) geq 10$ , ensuring a normal approximation.

Define population proportion ( $p$ ).

The true proportion of successes in the entire population.

What is the formula for the mean of the sampling distribution of $\hat{p}$ ?

$μ_{\hat{p}} = p$

What inequality represents the Large Counts Condition?

$np \geq 10$ and $n(1-p) \geq 10$

How do you calculate sample proportion ( $hat{p}$ )?

Number of successes in sample / Sample size