What is "sample proportion" in statistics?

Sample proportion is defined as the ratio of the number of times a certain event occurs to the total number of observations in a sample. It provides a way to quantify how common a certain characteristic or outcome is within a particular sample and is often denoted by the symbol ( p ) (or ( \hat{p} ) for an estimated sample proportion).

For example, if a researcher surveys 100 people and finds that 30 of them support a specific policy, the sample proportion of supporters is calculated as ( 30/100 = 0.30 ), or 30%. This proportion is useful for estimating the population parameter and conducting further statistical analysis, such as hypothesis testing or constructing confidence intervals for the population proportion.

The other options do not accurately capture the essence of "sample proportion." The mean of a sample refers to the average value of the sample observations, whereas the total number of observations counts all instances without considering any specific event's occurrence. Lastly, variance measures the spread of the sample data around the mean, not the occurrence of specific events.