AP Statistics Unit 3 Review

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What is a normal distribution?

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A normal distribution is a continuous probability distribution characterized by a symmetric bell-shaped curve, where most of the observations cluster around the central peak and probabilities for values further away from the mean taper off equally in both directions.

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What is the mean in a data set?

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The mean is the average of a set of numbers, calculated by adding all the values together and dividing by the number of values.

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What is the standard deviation?

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The standard deviation is a measure of the amount of variation or dispersion in a set of values. A low standard deviation indicates that the values tend to be close to the mean, while a high standard deviation indicates that the values are spread out over a wider range.

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What does it mean to assume a distribution is normal?

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Assuming a distribution is normal means that we are using the properties of the normal distribution to make inferences about the data, such as calculating probabilities and confidence intervals.

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What is a z-score?

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A z-score is a statistical measurement that describes a value's relationship to the mean of a group of values, expressed in terms of standard deviations from the mean.

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What is the Central Limit Theorem?

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The Central Limit Theorem states that the sampling distribution of the sample mean will be normally distributed, regardless of the shape of the population distribution, as long as the sample size is sufficiently large.

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What is a confidence interval?

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A confidence interval is a range of values, derived from a data set, that is likely to contain the value of an unknown population parameter, with a certain level of confidence.

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What is a hypothesis test?

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A hypothesis test is a statistical method that uses sample data to evaluate a hypothesis about a population parameter.

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What is a Type I error?

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A Type I error occurs when the null hypothesis is true, but is incorrectly rejected. It is also known as a false positive.

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What is a Type II error?

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A Type II error occurs when the null hypothesis is false, but is incorrectly accepted. It is also known as a false negative.