What Are the Causes and Types of Non-Sampling Error?

Non-sampling error refers to any mistake in data not caused by the random selection of a sample. These errors can arise during data collection or processing and are often difficult to detect. Unlike issues related to sample size, these problems can exist even in a census where an entire population is surveyed. They can be random or systematic, with systematic errors being particularly problematic because they can affect the entire dataset. The presence of non-sampling errors can introduce bias and reduce the reliability of the information gathered.

Errors in Data Acquisition

Errors that occur during the initial collection of information are a source of flawed data. The integrity of any subsequent analysis, whether for a market research report or an internal financial audit, depends on the accuracy of this initial data capture.

Measurement error relates to inaccuracies from the tools used to gather data. This could be a poorly designed questionnaire with leading or ambiguous questions that steer a respondent toward a particular answer. For example, a question phrased as, “Don’t you agree that our new investment product is a secure choice for your retirement?” encourages an affirmative response. This also occurs with faulty physical instruments, such as an improperly calibrated device used to measure manufacturing output.

Respondent error stems from the person providing the information. Recall bias is a frequent problem where individuals struggle to accurately remember past events, such as detailing every minor medical expense from the previous year for an insurance claim. Social desirability bias also affects responses, as people may provide answers they believe are more socially acceptable, like overstating their income on a loan application or understating their personal debts.

The individual collecting the data can also introduce errors. An interviewer’s tone of voice, phrasing of a question, or non-verbal cues can influence how a person responds. For instance, an interviewer who seems rushed or disinterested might receive less thoughtful answers from a survey participant. An interviewer might also deviate from the script or record responses incorrectly, compromising the data’s quality.

Errors in Data Handling and Processing

After data is collected, it moves into a handling and processing stage where new types of errors can be introduced. These mistakes happen during the clerical and technical procedures that convert raw information into a structured dataset ready for analysis.

Data entry errors are among the most frequent processing mistakes. These are human errors, such as a clerk mistyping a number when transcribing information from paper surveys into a computer system. A simple mistake, like entering $50,000 instead of $5,000 for a sales transaction, can significantly skew financial reports and business decisions.

Coding errors represent another vulnerability in data processing. This type of error occurs when categorizing open-ended or qualitative responses. For example, if a market research survey asks customers to describe their experience in their own words, an employee must “code” these responses into predefined categories like “Positive,” “Negative,” or “Neutral.” If the employee misinterprets a sarcastic comment as positive, the data will not accurately reflect customer sentiment.

Mistakes can also be made in the logic used to prepare data for analysis, known as specification errors. This happens when creating new variables or transforming existing ones using programming or spreadsheet formulas. An analyst might make an error in the formula used to calculate a company’s quarterly profit margin from raw sales and expense data. This incorrect specification would produce a flawed metric.

Frame and Selection Errors

The composition of the group being studied is fundamental to the accuracy of any research, and errors in this area can undermine the entire effort. These problems arise from discrepancies between the intended population for a study and the actual group from which data is collected. Such issues can lead to a final dataset that is not representative.

Coverage error occurs when the list used to draw a sample, known as the sampling frame, does not accurately reflect the target population. This can happen through undercoverage, where parts of the population are missing from the frame, or overcoverage, where some individuals are listed multiple times. A classic example of undercoverage is using a landline telephone directory to survey a city’s residents; this frame excludes households that only use mobile phones.

Non-response error arises when there is a systematic difference between the people who participate in a study and those who do not. This is not merely about having a smaller sample size; it is about the bias introduced when non-respondents share common traits that differentiate them from respondents. For instance, if a survey about workplace satisfaction has a low response rate from employees in a specific high-stress department, the overall results will likely present an overly positive view of job satisfaction at the company.