Here we will learn about random sampling, including what random sampling is, how to take a random sample of data, and the advantages and disadvantages of this sampling method.
There are also random sampling worksheets based on Edexcel, AQA and OCR exam questions, along with further guidance on where to go next if you’re still stuck.
Random sampling is a type of sampling method.
To take a random sample, we list each individual member of the population, assign a unique number to each member, and use a random number generator or a random number table to select the number of pieces of data required for the sample size.
We use simple random sampling to choose the individual items of data within the population.
Each member of the sample has an equal chance of being selected, reducing bias and sampling error.
| Sampling method | Description | Example |
|---|---|---|
| Random sampling (for simple random sampling) | Gathering a representative sample from a population where each member in the population has an equal chance of being selected. | Using a random number generator to select students in a class to complete a task. |
Random sampling is also used for other sampling techniques such as stratified sampling.
Stratified sampling requires another sampling method such as a simple random sample to generate a random selection of data values once the data is divided into subgroups (or subsets). This means that each item of data has an equal probability of being chosen and each subgroup within the sample is represented proportionally to the whole population.
Other types of random sampling methods include: cluster sampling, stratified sampling, and systematic sampling.
There are also other types of sampling methods that do not require simple random sampling include: quota sampling, convenience sampling (non-random sampling), non-probability sampling, and snowball sampling.
Following a random sampling methodology has advantages and disadvantages:
| Advantages | Disadvantages |
|---|---|
| Results can be generalised for a population It is more time efficient than asking the entire population. Reduced bias. | Expensive. Time consuming. Not always possible if there is no sampling frame or list to sample from. |
If every member of the population is in the sample, there is no sampling error. As the sample gets smaller, or the methodology has introduced a selection bias, the sampling error becomes more significant as this means that the sample may not be representative of the population.
The more random a sample is, the smaller the sampling error.
Once a random sample is chosen, the next step is data collection where respondents offer data to fulfill the requirements of the questionnaire or survey (for example). The collected values within the sample then go through data analysis to find generalised results for the population.