LESSON 31 - SIMPLE RANDOM SAMPLING: DEFINITION & STEPS OF CONDUCTING SIMPLE RANDOM SAMPLING DESIGN
Based on RESEARCH METHODS CLASS WITH PROF. LYDIAH WAMBUGU's video on YouTube. If you like this content, support the original creators by watching, liking and subscribing to their content.
Simple random sampling design gives every population member an equal probability of inclusion, with selection driven by chance.
Briefing
Simple random sampling design is a probability sampling method where every member of a population has the same chance of being selected for a sample—selection happens entirely by chance. That equal-probability rule matters because it’s the foundation for producing an “unbiased” sample, letting researchers make more defensible inferences about the larger population when analyzing quantitative data.
The lesson first clarifies what “simple random sample” means in statistics: it’s a set of subjects drawn from a larger group, with the subjects chosen randomly. In practice, the design requires that each element (subject) has an identical probability of inclusion at any stage of the sampling process. The method is tied to the use of random numbers; without random numbers, researchers cannot justify that chance-based selection.
After establishing the definition, the lesson lays out the steps for conducting simple random sampling. A key prerequisite is determining the sample size before applying the sampling design. The example assumes a population of 500 and a sample size of 217 (referencing earlier work on how sample size is determined). Step one is to develop a sampling frame by numbering every population element from 1 to 500. Step two is to generate 217 random numbers using a procedure previously taught for producing random numbers (including the idea that random-number generation may be done via internet tools). Step three is to draw the 217 subjects/respondents from the sampling frame by matching the generated random numbers to the numbered population list. The lesson illustrates this with selected numbers such as 259, 221, 77, 412, 336, 180, 119, 243, 147, and 79—each number corresponds to a person in the sample.
The lesson then weighs strengths and limitations. A major strength is that simple random sampling is easy to use, particularly when the sampling frame is accurate. It also reduces selection bias because every population member is given a chance to be selected. The method is described as accessible even for researchers with limited experience, provided the sampling frame is reliable.
Limitations focus on practical constraints and potential errors. One challenge is that obtaining a precise, accurate sampling frame can be difficult. Another risk is sampling error if the selected sample fails to reflect the population properly. The process can also be time-consuming and costly when populations are large and diverse, since generating random numbers and selecting respondents requires effort. Finally, while the design aims for unbiased selection, sampling bias can still occur if the sample ends up not being inclusive enough—such as when not every member of the population is actually represented in the sampling frame.
The lesson closes by positioning simple random sampling as the first random probability sampling technique, with stratified random sampling introduced as the next topic.
Cornell Notes
Simple random sampling design selects a sample so that every population member has an equal chance of being included, with selection driven by chance rather than researcher preference. The method depends on a complete, accurate sampling frame and the generation of random numbers to choose respondents. The lesson’s example uses a population of 500 and a sample size of 217: elements are numbered 1–500, 217 random numbers are generated, and each random number maps to a specific respondent in the sample. Strengths include ease of use and reduced selection bias when the sampling frame is accurate. Limitations include difficulty accessing a precise sampling frame, possible sampling error, time and cost for large populations, and the possibility of bias if the sample is not truly inclusive.
What makes simple random sampling “simple,” and what condition must be true for it to work properly?
What are the three core steps for conducting simple random sampling in the lesson’s example?
Why does the method require random numbers rather than manual selection?
What strengths does the lesson attribute to simple random sampling?
What limitations and risks are highlighted, even though the design aims for unbiased sampling?
Review Questions
- In your own words, what does “equal chance of being selected” mean in simple random sampling, and how is that equality enforced?
- Walk through the steps of simple random sampling using the example of a population of 500 and sample size of 217.
- List at least three limitations of simple random sampling and explain how each limitation could affect study results.
Key Points
- 1
Simple random sampling design gives every population member an equal probability of inclusion, with selection driven by chance.
- 2
A complete and accurate sampling frame is essential; otherwise, equal-probability selection can fail in practice.
- 3
Determine sample size before applying the sampling design so the correct number of respondents can be selected.
- 4
Number all population elements (e.g., 1 to 500) to create a sampling frame that can be matched to random numbers.
- 5
Generate random numbers equal to the intended sample size and select respondents by mapping each random number to the numbered list.
- 6
Simple random sampling is easy to use and can reduce selection bias when the sampling frame is reliable.
- 7
Sampling error, time/cost burdens, and bias can still occur if the sampling frame is inaccurate or the sample is not truly inclusive.