# What is a sampling distribution in statistics?

**sampling distribution**is a probability

**distribution**of a

**statistic**obtained through a large number of samples drawn from a specific population. The

**sampling distribution**of a given population is the

**distribution**of frequencies of a range of different outcomes that could possibly occur for a

**statistic**of a population.

Keeping this in consideration, how do you describe a sampling distribution?

A **sampling distribution** is where you take a population (N), and find a statistic from that population. The “standard deviation of the **sampling distribution** of the proportion” means that in this case, you would calculate the standard deviation. This is repeated for all possible **samples** from the population.

**sampling distribution**or finite-

**sample distribution**is the probability

**distribution**of a given random-

**sample**-based statistic.

**Sampling distributions**are important in statistics because they provide a major simplification en route to statistical inference.

In this manner, what is the difference between a sample distribution and a sampling distribution?

It is theoretical **distribution**. The **distribution of sample** statistics is called **sampling distribution**. For example, If you draw an indefinite number **of sample of** 1000 respondents from the population the **distribution of** the infinite number **of sample** means would be called the **sampling distribution of** the mean.

**Response distribution** refers to how you expect people to respond to the survey questions. If sample data is skewed highly to one end, the population probably is too. If you don't know, use 50%.