When doing statistical analysis, it is impractical and often impossible to gather all the available data. That is when you would seek to sample the data to analyze it and make decisions about your process.
In statistics, a sample refers to a group of individuals, objects, or events that are selected from a larger population for the purpose of making inferences or drawing conclusions about that population. Samples are used when it is not feasible nor practical to study an entire population.
The benefits and drawbacks of using samples are as follows:
- Saves time and money
- Allows for more meaningful data
- Simplifies measurement over time
- Can improve accuracy
- Accept a degree of uncertainty and sampling error for not measuring the whole population
Overview: What is a sample?
A sample is typically selected using a random sampling technique, which ensures that each member of the population has an equal chance of being included in the sample. You may also use:
- Stratified random sampling
- Select a random sample within a stratified category or group
- Sample sizes for each group should be proportional to the relative size of the group in the population
- Systematic or periodic sampling
- Sample every nth one (e.g., select every 5th one)
- Determine sampling frequency or how much time between samples
- Watch out for bias as a result of your selected time frequency
- Systematic and periodic sampling
- Sample consecutive samples every nth time period.
The size of the sample is an important consideration in statistical analysis, as larger samples generally provide more accurate and representative results than smaller samples. It is important to note that the results obtained from a sample can only be generalized to the population if the sample is representative of the population. A representative sample is one in which the characteristics of the sample closely match those of the population. You will use one of the above sampling strategies to accomplish that.
An industry example of using a sample
The laboratory in a hospital was interested in the actual utilization of the lab equipment. Lab techs had been complaining that they needed new equipment because they were delayed in doing tests because of an inadequate number of lab machines.
The hospital’s Black Belt (BB) decided to use working sampling to get an estimate of utilization since it was not practical to watch and capture data on all the equipment during the day. A schedule was created for the BB to go out and record whether the equipment is in use or not.
This schedule was a list of random times during the day shift. The BB would go out during that random time and observe whether the equipment was being used or was idle. At the end of a week’s data collection, it was determined that utilization was not as high as people had presumed and no additional equipment was required.
Frequently Asked Questions (FAQ) about samples
What is a random sample?
A random sample is a sample in which each member of the population has an equal chance of being selected. This ensures that the sample is representative of the population and reduces the risk of bias in the sample.
How do I determine the size of my sample?
The size of the sample depends on several factors, including the size of the population, the level of precision desired, and the variability of the data. A larger sample size generally provides more accurate and representative results, but also requires more resources to obtain.
What is a sampling bias?
A sampling bias occurs when the sample is not representative of the population, usually due to a non-random sampling method or a sample selection process that favors certain characteristics of the population.