Sampling Distributions & Confidence Intervals for a Proportion

These flashcards cover the vocabulary related to sampling distributions and confidence intervals for proportions, aiding in understanding key concepts necessary for the exam.

Sampling Variability

The concept that sample proportions will vary from sample to sample.

Sample Proportion (p-hat)

The number of successes in a sample divided by the total number in the sample.

Assumptions and Conditions

Requirements that must be met for the sampling distribution model to be valid, including independence, sample size, and randomization.

Standard Error

The estimated standard deviation of the sample proportion.

Confidence Interval (CI)

An interval estimate of a population parameter calculated so that the true value is likely to fall within this range.

Margin of Error (ME)

The amount of value above and below the point estimate that defines the width of the confidence interval.

Unbiased Estimator

A sample statistic whose mean value is equal to the population parameter being estimated.

Normal Model

A model that can be used if the sample size is large enough and the sample proportions fulfill certain conditions.

Probability

A measure of the likelihood that an event will occur.

Point Estimate

A single number based on sample data that represents a feasible value of the population parameter.

Random Sample

A subset of a population selected such that every individual has an equal chance of being selected.

Z-score

A measure of how many standard deviations an element is from the mean.

Sampling Distribution Model

A probability distribution of all possible sample proportions for a given sample size.

Confidence Level

The percentage indicating how confident we are that the true population parameter is captured in the confidence interval.

Statistical Independence

The condition where the outcome of one event does not affect the outcome of another.

True Proportion (p)

The actual proportion of a certain characteristic in the entire population.

Histogram

A graphical representation of the distribution of numerical data.

Binomial Experiment

A statistical experiment that has two possible outcomes (success or failure) for each trial.

Population Size

The total number of individuals or items that can be selected from when obtaining a sample.