A population consists of all the members of a group of interest. Since it may be impractical to examine every member of a population, a sample — a subset of the population — is sometimes selected to represent the population. The sample can then be analyzed to draw conclusions about the entire population.

Let's consider our situation.

At a bridal shower, a sticker was placed at the bottom of three random plates. The guests who receive the stickered plates win a prize.

Here, the population consists of the guests who attended the bridal shower. The sample consists of the guests who received the stickered plates. Samples can be classified into different categories.

Name	Characteristic
Simple Random Sample	Each member of the population has an equal chance of being selected.
Systematic Sample	Members are selected according to a specified interval from a random starting point.
Self-selected Sample	Members volunteer to be included in the sample.
Convenience Sample	Members that are readily available or easy to reach are selected.
Stratified Sample	The population is first divided into similar, nonoverlapping groups. Members are then randomly selected from each group.

In our situation, all guests have an equal chance of receiving a stickered plate. This means that we have a simple random sample. Let's finally summarize our answers in a table.

Sample	Guests who received the stickered plates
Population	All guests
Classification	Simple random sample
Explanation	All guests had an equal chance of receiving a stickered plate

{{ 'ml-heading-exercise' | message }} {{ exercise.name }} {{ 'ml-label-page' | message }} {{ exercise.page }}

{{ 'ml-solutions-missing-heading' | message }}