Statistics for Business - Sampling, Estimation

A comprehensive set of flashcards covering key concepts in sampling and estimation in statistics.

Statistical Population

All the units (items) being the object of a study; collection of all elements of interest.

Sampling

The process of selecting a subset of elements from a population for the purpose of making inferences about the population.

Sample

A set of elements chosen (drawn) from the population.

Probability Sample

A sample chosen based on randomness, where every element has the same chance of being selected.

Sampling Errors

Errors that can be measured and calculated that occur in the sampling process.

Non-Sampling Errors

Errors that are difficult to measure but can affect the results, such as measurement errors or non-response.

Simple Random Sampling (SRS)

Sampling from a homogenic, finite population without replacement where every element has an equal chance to be selected.

Stratified Sampling

Sampling from a heterogenic population divided into strata, with SRS used within each stratum for better estimation accuracy.

Cluster Sampling

Sampling from a homogeneous, finite population by grouping elements into clusters and randomly selecting clusters to sample from.

Systematic Sampling

Selecting elements from a sorted population at regular intervals.

Point Estimator

A statistic that provides a single value estimate of a population parameter.

Law of Large Numbers

The average of results from a large number of trials should be close to the expected value.

Central Limit Theorem

The distribution of the sum of a large number of independent random variables tends toward a normal distribution.

Confidence Interval

An interval estimate that is likely to contain the parameter with a specified level of confidence.

Margin of Error

The maximum expected difference between the true population parameter and a sample estimate.

Sample Size Determination

The process of calculating the number of observations or replicates to include in a statistical sample.