All MCQs

A probability distribution specifies the possible outcomes of a random variable and their probabilities or densities. It provides a mathematical model of uncertainty.

Estimating a population mean from a sample involves reasoning beyond the observed data. The other tasks summarize only the collected sample.

Statistical inference uses sample data to estimate, test, or predict population characteristics. It extends beyond merely describing the observations collected.

Representativeness means the sample reflects relevant characteristics of the target population. This improves the credibility of generalizing sample results.

A census attempts to collect information from every member of the defined population. The other options observe only subsets of their populations.

Random samples naturally differ because they contain different units from the population. Such sample-to-sample fluctuation is called sampling variability or sampling error.

Selection bias occurs when the inclusion process systematically favors some population members over others. Visitors to a broadband website are unlikely to represent people with limited internet access.

Simple random sampling assigns each population member an equal known opportunity to be selected. The other procedures can systematically favor particular employees.

A sampling frame is the operational list or source from which sample units are selected. Its coverage affects whether the sample can represent the target population.

A statistic is a numerical summary calculated from sample data. It can be used to estimate the corresponding population parameter.

A parameter is a numerical characteristic of an entire population. Because the value describes all households, it is not merely a sample statistic.

A sample is the subset of population units actually observed or measured. The city’s full set of households is the target population.