# Probability Sampling

Probability Sampling is a sampling technique in which sample from a larger population are chosen using a method based on the theory of probability. For a participant to be considered as a probability sample, he/she must be selected using a random selection.

The most important requirement of probability sampling is that everyone in your population has a known and an equal chance of getting selected. For example, if you have a population of 100 people every person would have odds of 1 in 100 for getting selected. Probability sampling gives you the best chance to create a sample that is truly representative of the population.

Probability sampling uses statistical theory to select randomly, a small group of people (sample) from an existing large population and then predict that all their responses together will match the overall population.

Probability Sampling Example

Let us take an example to understand this sampling technique. The population of the US alone is 330 million, it is practically impossible to send a survey to every individual to gather information but you can use probability sampling to get data which is as good even if it is collected from a smaller population.

For example, consider hypothetically an organization has 500,000 employees sitting at different geographic locations. The organization wishes to make certain amendment in its human resource policy, but before they roll out the change they wish to know if the employees will be happy with the change or not. However, it’s a tedious task to reach out to all 500,000 employees. This is where probability sampling comes handy. A sample from the larger population i.e from 500,000 employees can be chosen. This sample will represent the population. A survey now can be deployed to the sample.

From the responses received, management will now be able to know whether employees in that organization are happy or not about the amendment.

#### Steps involved in Probability Sampling

1. Choose your population of interest carefully: Carefully think and choose from the population, people you think whose opinions should be collected and then include them in the sample.
2. Determine a suitable sample frame: Your frame should include a sample from your population of interest and no one from outside in order to collect accurate data.
3. Select your sample and start your survey: It can sometimes be challenging to find the right sample and determine a suitable sample frame. Even if all factors are in your favor, there still might be unforeseen issues like cost factor, quality of respondents and quickness to respond. Getting a sample to respond to true probability survey might be difficult but not impossible.

But, in most cases, drawing a probability sample will save you time, money, and a lot of frustration. You probably can’t send surveys to everyone but you can always give everyone a chance to participate, this is what probability sample is all about.

When to use Probability Sampling

1. When the sampling bias has to be reduced: This sampling method is used when the bias has to be minimum. The selection of the sample largely determines the quality of the research’s inference. How researchers select their sample largely determines the quality of a researcher’s findings. Probability sampling leads to higher quality findings because it provides an unbiased representation of the population.
2. When the population is usually diverse: When your population size is large and diverse this sampling method is usually used extensively as probability sampling helps researchers create samples that fully represent the population. Say we want to find out how many people prefer medical tourism over getting treated in their own country, this sampling method will help pick samples from various socio-economic strata, background etc to represent the bigger population.
3. To create an accurate sample: Probability sampling help researchers create an accurate sample of their population. Researchers can use proven statistical methods to draw accurate sample size to obtained well-defined data.