Comparison of Probability Sampling and Non-Probability Sampling Methods

Probability sampling and non-probability sampling are two different approaches to selecting samples from a population for research or data collection. They have distinct characteristics and implications for the validity and generalizability of the results.

1. Probability Sampling

Probability sampling involves randomly selecting samples from a population, ensuring that every individual or element in the population has a known and non-zero chance of being included in the sample. This approach allows researchers to make statistical inferences and draw conclusions about the entire population based on the characteristics of the sample.

There are four main types of probability sampling:

Sampling Method Description Example
Simple Random Sampling Each individual in the population has an equal chance of being selected. Drawing names from a hat or using random numbers.
Stratified Sampling The population is divided into subgroups (strata), and a random sample is taken from each stratum. Selecting students randomly from each grade level.
Systematic Sampling Researchers select every nth individual from the population. Choosing every 10th customer entering a store.
Cluster Sampling The population is divided into clusters, and a random sample of clusters is selected. Randomly selecting a few schools from a city.

 

2. Non-probability Sampling

Non-probability sampling involves selecting samples from a population without following a random selection process, leading to unknown or unequal chances of individuals being included. This approach may still provide valuable insights but cannot support statistical inference to the broader population.

There are four main types of non-probability sampling:

Sampling Method Description Example
Convenience Sampling Researchers select individuals who are readily available and convenient. Surveying people in a shopping mall.
Judgmental Sampling Researchers use their judgment to select individuals who fit specific criteria. Handpicking participants with certain expertise.
Snowball Sampling Existing participants refer other potential participants, forming a chain. Recruiting participants through referrals.
Quota Sampling Researchers set quotas for different groups and non-randomly fill them. Surveying a specific number of men and women.

 

Table summary

Criteria Probability Sampling Non-Probability Sampling
Selection Process Random selection from the population. Non-random, often based on convenience or researcher judgment.
Representative Likely to be representative of the population. May not be representative; results cannot be generalized easily.
Statistical Inference Possible to draw statistical inferences to the population. Cannot make statistical inferences to the population.
Sample Accuracy Usually higher accuracy and reliability. Potential for biased results and lower accuracy.
Examples Simple Random Sampling, Stratified Sampling, Systematic Sampling, Cluster Sampling. Convenience Sampling, Judgmental Sampling, Snowball Sampling, Quota Sampling.