Sample and Non-Sample Errors

Sample and Non-Sample Errors - Non-sampling random errors, Non-sampling systematic errors

Exhibit 34.8   Inaccuracies due to systematic (bias) and random errors.

Errors in research data can be broadly categorized into sample and non-sample errors. Sample errors arise from the inherent inaccuracies that occur when working with a sample of the target population. These errors can be minimized by increasing the sample size or improving the sample design. Accuracy standards are often set based on acceptable levels of error within a given confidence interval.

Apart from sample errors, research is also subject to non-sampling errors. These errors occur due to various factors and can be both systematic and random in nature. Non-sampling errors are challenging to measure and quantify accurately.

Non-sampling random errors (shown in Exhibit 34.8, at the centre) are unpredictable errors that arise during data collection and processing. They can result from mistakes made by interviewers, incorrect responses from respondents, or errors in data computation during processing. These errors contribute to increased variability in the results, but they tend to cancel out when the sample size is large.

Non-sampling systematic errors (shown in Exhibit 34.8, on the left) accumulate across the entire sample and can lead to results that are not representative of the population. These errors can arise from factors such as poorly worded questions that inadvertently influence responses or issues with coverage, where certain segments of the target population cannot be reached. Systematic errors are of greater concern as they introduce bias into the final results. Unlike sampling inaccuracies, increasing the sample size does not reduce bias resulting from systematic errors.

To minimize bias, the nature of the study needs to be carefully considered. For example, in shopper trend studies, respondents should be selected in a way that neutralizes bias arising from their proximity to shopping outlets, as shopping habits are heavily influenced by store location.

Non-sampling error can be reduced by maintaining reliable survey frames, designing and testing questionnaires carefully, providing thorough training to the data collection team (interviewers, auditors), implementing monitoring and call-back procedures, and maintaining high standards in measurement and data processing systems.


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