SAMPLING DISTRIBUTION –ALL YOU NEED TO KNOW
Sampling distribution is a statistic that determines the probability of an event based on data from a small group within a large population. Its primary purpose is to establish representative results of small samples of a comparatively larger population. Since the population is too large to analyze, the smaller group is selected and repeatedly sampled, or analyzed. The gathered data, or statistic, is used to calculate the likely occurrence, or probability, of an event. Using a sampling distribution simplifies the process of making inferences, or conclusions, about large amounts of data.
The idea behind a sampling distribution is that when you have a large amount of data (gathered from a large group, the value of a statistic from random samples of a small group will inform you of that statistic’s value for the entire group. Once the data is plotted on a graph, the values of any given statistic in random samples will make a normal distribution from which you can draw inferences.
There are three primary factors that influence the variability of a sampling distribution.
- The number observed in a population: This variable is represented by “N.” It is the measure of observed activity in a given group of data.
- The number observed in the sample: This variable is represented by “n.” It is the measure of observed activity in a random sample of data that is part of the larger grouping.
- The method of choosing the sample: How the samples were chosen can account for variability in some cases.
Types of Sampling Distributions
1. Sampling distribution of mean
The most common type of sampling distribution is of the mean. It focuses on calculating the mean of every sample group chosen from the population and plotting the data points. The graph shows a normal distribution where the center is the mean of the sampling distribution, which represents the mean of the entire population.
2. Sampling distribution of proportion
This sampling distribution focuses on proportions in a population. Samples are selected and their proportions are calculated. The mean of the sample proportions from each group represent the proportion of the entire population
3. T-distribution
A T-distribution is a sampling distribution that involves a small population or one where not much is known about it. It is used to estimate the mean of the population and other statistics such as confidence intervals, statistical differences and linear regression. The T-distribution uses a t-score to evaluate data that wouldn’t be appropriate for a normal distribution.
