Sampling Error:
Sampling error is the difference between the characteristics of a sample and the characteristics of the entire population from which the sample was drawn.
It occurs because a sample represents only a subset of the population, and not every member of the population is included in the sample. Sampling error is a natural and expected part of the sampling process and can be quantified and controlled through statistical methods. The larger the sample size, the smaller the sampling error tends to be.
Two Major Non-Sampling Errors:
- Selection Bias:
- Description: Selection bias occurs when the sample is not representative of the population due to systematic errors in the selection process. This bias can result from factors such as non-random sampling methods, non-response bias, or the exclusion of certain groups from the sampling frame.
- Example: If a survey is conducted only during weekdays, it may exclude individuals who work on weekdays but have different characteristics than those available for the survey on weekdays, leading to selection bias.
- Measurement Error:
- Description: Measurement error occurs when there are inaccuracies or inconsistencies in the way data is collected or measured. This can result from instrument limitations, respondent misunderstandings, or errors in recording and data entry.
- Example: In a survey that relies on self-reported income, respondents may unintentionally provide inaccurate information due to memory lapses, social desirability bias, or difficulty recalling exact figures. This introduces measurement error into the data.
It’s important to note that while sampling error is inherent in the sampling process, non-sampling errors are related to issues in the data collection and analysis phases. Both sampling and non-sampling errors can impact the validity and reliability of study findings, so researchers take steps to minimize and account for these errors in their research designs and analyses.