What is a Sampling Error and How to Avoid It
A sampling error is a statistical discrepancy that arises when an analyst selects a sample that does not accurately represent the entire population of data. This leads to results in the sample that diverge from what would have been obtained from the entire population.
Sampling involves choosing a number of observations from a larger population. Various methods of selection can lead to both sampling errors and non-sampling errors.
Key Takeaways
- Sampling errors occur when the study sample isn’t representative of the whole population.
- Sampling is done by selecting some observations from the larger population.
- Even randomized samples have some degree of sampling error as they only approximate the population from which they are drawn.
- Increasing the sample size can reduce the prevalence of sampling errors.
- Sampling errors can be classified into four main categories: population-specific error, selection error, sample frame error, and non-response error.
Delving Deeper into Sampling Errors
Sampling errors emerge when there is deviation between the sample value and the true population value. Such errors occur because the sample is not representative of the population or is biased in some fashion. Even randomized samples will exhibit some sampling error since they approximate, rather than perfectly reflect, the population.
Calculating Sampling Error
The sampling error formula is used in statistical analysis to calculate overall sampling error. This formula involves dividing the population’s standard deviation by the square root of the sample size, and then multiplying the result by the Z-score value, based on the confidence interval.
1Sampling Error = Z × (σ / sqrt(n))
2where:
3- Z = Z score value based on the confidence interval (~1.96)
4- σ = Population standard deviation
5- n = Sample size
Types of Sampling Errors
Population-Specific Error
This error occurs when a researcher lacks understanding of who should be surveyed.
Selection Error
Selection error happens when the survey participants self-select, leading to skewed results. This can be mitigated by encouraging broader participation.
Sample Frame Error
A sample frame error arises when selecting from wrong population data.
Non-response Error
Non-response error occurs when researchers can’t obtain responses from potential participants or if they refuse to respond.
Reducing Sampling Errors
You can reduce sampling errors by increasing the sample size. A larger sample size makes the sample closer to the actual population, thereby minimizing deviations. Utilize random sampling techniques as an additional measure to obtain a more representative sample. For example, a systematic approach where a researcher picks every 10th person on a list can be effective.
Examples of Sampling Errors
Consider XYZ Company, which offers a subscription-based video streaming service. The company wants to survey homeowners who watch at least 10 hours of streaming weekly to see if there’s interest in a lower-priced subscription. If XYZ isn’t meticulous in the sampling process, several sampling errors could occur.
Population-Specific Error
If XYZ targets people aged 15-25, many may not decide about streaming purchases. Alternatively, targeting working adults who make purchase decisions but don’t watch 10 hours of programming introduces another type of error.
Selection Error
Selection error could arise if relying solely on participants who immediately respond. Following up with non-responders could provide more accurate results.
Sampling Error vs. Non-sampling Error
Sampling errors occur due to particular sample choices, while non-sampling errors arise from human errors in data collection. For instance, including a group only watching five hours of video programming weekly is a non-sampling error.
Why Is Sampling Error Important?
Awareness of sampling errors is critical for gauging the confidence level in research results. Knowing about potential sampling errors informs about how much variation to expect in research outcomes.
Finding the Sampling Error
Quantifying the exact sampling error isn’t feasible as obtaining data from the entire population isn’t typically possible. This necessity for representative samples inherently leads to sampling errors.
Sampling Error vs. Standard Error
Sampling error derives from the standard error by multiplying it with a Z-score value for the confidence interval.
Conclusion
Sampling errors happen when the drawn sample differs from the true population. Major sampling errors lead to incorrect population estimates or inferences. Mitigating errors involves understanding their types and implementing strategies, such as increasing sample size and utilizing random sampling, to ensure a representative sample and dependable survey outcomes.
Related Terms: non-sampling error, sample bias, confidence interval, population standard deviation.
