Sampling Distribution

Flashcards related to key terms and concepts in sampling distribution and statistics.

Population Parameter

A numerical measure such as mean, median, mode, range, variance, or standard deviation calculated for population data, usually written with Greek letters.

Sample Statistic

A summary measure calculated for a sample data set, usually written with Latin letters.

Sampling Variability

The value of a statistic varies from sample to sample.

Sampling Distribution

The distribution of all the values of a statistic.

Sample Proportion (𝑝̂)

Calculated by taking the number of successes in a sample divided by the sample size.

Central Limit Theorem (CLT)

States that means of random samples tend to have an approximately normal distribution as the sample size increases.

Standard Error

The standard deviation of a sampling distribution.

Independence Assumption

The sampled values must be independent of each other.

Sample Size Assumption

The sample size must be sufficiently large.

Randomization Condition

The sample should be a simple random sample of the population.

Success/Failure Condition

The sample size has to be big enough so that both np and nq are at least 10.

Sampling Error

The difference between the sample statistic and the population parameter.

Histogram of Sample Proportions

Expected shape of sampling distribution; centered around the true population proportion.

Sampling Distribution of the Sample Mean

If ȳ is the sample average drawn from a population, the sampling distribution of ȳ has a mean of µ and a standard deviation of σ/√n.