Sampling Methods in Statistics

It's almost next to impossible to survey large numbers - all citizens of a country, all women under 35, all business owners in a state - and therefore statisticians use samples with defined criteria to make observations and report findings.

Instead of collecting data from the entire population, a portion of the population is surveyed for any study. The observations from the sample are extrapolated for the population.

This is why it's important to select the correct sample and the correct questions to be asked to them. If this is not done, the study’s objectives will not be met or incorrect responses will be obtained.

Clear establishment of survey objectives, correct sample identification, the data that needs to be collected, and level of precision required in the study all contribute to sample selection.

In this article, we will learn about sampling methodology. Probability sampling and non-probability sampling are the two types of sampling. In probability sampling every element in the sample has the probability of being picked for the study, unlike non-probability sampling.

Probability Sampling

Here the sample is chosen randomly as one would in a lottery. There are different types of probability sampling.

Simple Random Sampling or SRS

In this type of sampling, all units in the sample have an equal chance of being selected. A ready list of units is used to select the sample. Names from a telephone directory, a voters list in a geographical area, social security card holders in a district could be used for a sample. Care is taken not to repeat units in the same to arrive at a unique randomized list. This method is useful when such lists are available. Otherwise it becomes expensive to formulate the list.

A supermarket plans to offer a discount on 15 items that they stock. They generate a list that contains 50 of the most picked up items for the same time period last year. Slips containing these 50 items are put in a box and then 15 of them are picked up. So the probability of any of the 50 items being picked up is sample size divided by population size or 15 /50, which is 0.3 or 3 in 10 chances of being picked.

Systematic Sampling

In this type of sampling, the units in the sample have a fixed interval. Let us look at a manufacturing line of spark plugs. The supervisor needs to test a batch for quality. He decides to take a sample size of 200. The total population of spark plugs produced in a day is 1000. So the sample interval will be Population/ Sample Size. That is 1000/200 which is equal to 5. The spark plugs will be picked up for testing with an interval of 5. The supervisor may choose to start with the first spark plug then the order of selection will be 1, 6, 11, 16, 21 and so on. If he chooses the second spark plug as the starting point then the order of selection is 2, 7, 12, 17 and so on.