Parameters, Samples and Populations:

In statistics, parameters, samples, and populations are essential concepts that are used to analyze data. These concepts help in making inferences about the population based on the sample data. Let's understand what these terms mean.

Population:
A population is a collection of all the individuals, objects, or events that we are interested in studying, and it includes all the possible observations that we could make. For example, if we are interested in studying the heights of all the students in a school, then the population would be all the students in that school.

Sample:
A sample is a subset of the population that we select to study. It is a smaller group of individuals, objects, or events that we use to make inferences about the population. The sample should be representative of the population to avoid bias. For example, if we are interested in studying the heights of all the students in a school, then we could randomly select a sample of 50 students from that school.

Parameter:
A parameter is a characteristic of a population that describes some aspect of the population. It is a numerical value that summarizes the entire population. For example, the average height of all the students in a school is a parameter.

Explanation of the Answer:

Based on the above definitions, we can say that a parameter is a characteristic of a population. It is a numerical value that describes some aspect of the population. Therefore, the correct answer is option 'A', which states that a parameter is a characteristic of a population. Option 'B' is incorrect because a sample is not a characteristic of a population. Option 'C' is incorrect because a probability distribution is a mathematical function that describes the likelihood of different outcomes in a random experiment. Option 'D' is incorrect because a parameter is a characteristic of a population, and a sample is a subset of a population.