All Flashcards

Explain the concept of random variation.

Data points are scattered with no discernible pattern, indicating pure chance.

Explain the importance of the normal curve in statistical inference.

It allows us to calculate probabilities in sampling distributions and standardize data using z-scores.

Explain the meaning of 'normal' in statistics.

Refers to a specific bell-shaped curve, not necessarily 'typical' or 'average'.

Explain the purpose of checking the Large Counts Condition.

To ensure that the sampling distribution is approximately normal, allowing for valid statistical inference.

Explain how z-scores are used with the normal curve.

Z-scores standardize sample data, allowing us to use the normal curve to determine probabilities.

What are the differences between random and non-random variation?

Random: No discernible pattern, pure chance | Non-random: Underlying pattern, due to measurement error/bias.

What are the differences between successes and failures in the Large Counts Condition?

Successes: $np \ge 10$ , expected number of successes is at least 10 | Failures: $n(1-p) \ge 10$ , expected number of failures is at least 10.

What is the formula to check for large counts condition (successes)?

$np \ge 10$ , where $n$ = sample size and $p$ = probability of success.

What is the formula to check for large counts condition (failures)?

$n(1-p) \ge 10$ , where $n$ = sample size and $p$ = probability of success.

What is the formula for calculating the z-score?

$z = \frac{\hat{p} - p}{\sqrt{\frac{p(1-p)}{n}}}$

What is the formula for calculating the sample proportion?

$\hat{p} = \frac{x}{n}$ where $x$ is the number of successes and $n$ is the sample size.