What type of error is caused by data collection methods such as misinterpretation?

The type of error caused by data collection methods like misinterpretation is classified as a non-sampling error. Non-sampling errors occur due to factors other than the random chance associated with selecting a sample. These errors can arise from mistakes made during the data collection process, such as misinterpretation of questions, issues with survey design, or errors in data entry.

Misinterpretation specifically affects the reliability and validity of the collected data, as it leads to respondents providing answers that do not accurately reflect their true thoughts or behaviors. This can happen due to ambiguous wording or context that fails to convey the intended meaning of survey questions. Therefore, non-sampling errors encompass a wide range of issues that can skew results, rather than the inherent variability present in sampling, which is acknowledged in a different category.

Other types of errors, such as sampling errors, arise purely due to the selection of elements from a population and are expected to diminish as sample size increases. Random errors occur by chance and can affect data unpredictably. Systematic errors, while related to bias rather than misinterpretation, pertain more to consistent inaccuracies that can be traced back to specific flawed methodologies or biases in the data collection process. Thus, it’s clear that non-sampling errors,