Step 1: Understanding the Concept:
In inferential statistics, researchers use sample data to make estimates or draw conclusions about a larger population.
A population parameter (such as the true population mean, \( \mu \)) is often unknown and must be estimated using sample statistics.
Step 2: Detailed Explanation:
Let us define the statistical terms listed in the options:
- Statistic:
Any quantitative measure calculated from a sample of data (e.g., the sample mean, \( \bar{x} \), or sample variance, \( s^2 \)).
- Variable:
A characteristic or attribute that can take on different values among individuals in a population (e.g., fish length or weight).
- Estimator:
The mathematical formula, rule, or function used to estimate an unknown population parameter (e.g., the formula for calculating the sample mean, \( \bar{x} = \frac{\sum x_i}{n} \)).
- Point Estimate:
The specific, single numerical value calculated from a sample that is used as the best estimate of a population parameter.
For example, if we measure a sample of fish and calculate a mean length of 24.5 cm, that single value (24.5 cm) is a point estimate of the true average length of the entire fish population.
Unlike an interval estimate (which provides a range of likely values, such as a confidence interval), a point estimate provides a single value.
Step 3: Final Answer:
A single value used to estimate a population parameter is called a point estimate.
Thus, the correct choice is (B).