AP Statistics TPS 7.3

Front

What is the Central Limit Theorem?

Back

The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution.

Front

When should you use the Normal Distribution?

Back

Use the Normal Distribution when the population is normally distributed or when the sample size is large enough (typically n > 30) for the Central Limit Theorem to apply.

Front

What is the mean of a normal distribution?

Back

The mean of a normal distribution is the central value around which the data is symmetrically distributed.

Front

What is the standard deviation in a normal distribution?

Back

The standard deviation measures the dispersion or spread of the data points around the mean in a normal distribution.

Front

How does increasing sample size (n) affect the sampling distribution of sample means?

Back

As the sample size increases, the sampling distribution of sample means becomes narrower and more normal.

Front

What is the probability of selecting a value greater than the mean in a normal distribution?

Back

The probability of selecting a value greater than the mean in a normal distribution is 0.5 or 50%.

Front

What is the significance of a skewed distribution?

Back

A skewed distribution indicates that the data is not symmetrically distributed, affecting the mean and median values.

Front

What does it mean if a distribution is skewed left?

Back

If a distribution is skewed left, it means that the left tail is longer or fatter than the right tail, indicating that there are more lower values.

Front

What is the formula for calculating the z-score?

Back

The z-score is calculated using the formula: z = (X - μ) / σ, where X is the value, μ is the mean, and σ is the standard deviation.

Front

What is the role of the standard error in statistics?

Back

The standard error measures the accuracy with which a sample represents a population, calculated as the standard deviation divided by the square root of the sample size.