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Sampling error is related solely to the sample taken for conducting a census of the population (or data).
There can be errors where the sample is too small or does not represent the characteristics of the whole population (or data type).
Such errors can be reduced by taking random samples where each unit of the selected data and the change is measured.
Non-sampling errors can be random or systematic.
Random means unpredictable, leading to wrong estimations and systems accumulating over the real data.
It can result in bias in the results.
It is caused by other factors like coverage error, where the unit in the sample is incorrectly excluded or included, or duplicated.
Non-response refers to the failure to get an accurate response from the sample due to refusal, non–contact, absence or other factors.
It can be completed if the respondent is unavailable, temporarily absent, or refuses to participate in the survey.
It can be partial when the survey participants cannot clearly understand the questions.
Processing error can happen when there is an error in how the data is entered, edited or coded in the system, leading to inaccurate outputs.
Response error can happen when the respondents give erroneous responses intentionally or accidentally.
It can happen that the questions or instructions are not clear, the respondents cannot recall the exact information, or sometimes, the queries are just answered in a socially desirable manner.
The interviewers can influence how the respondent answers as if they are too friendly or aloof; it can change the answer per the interviewer's response. Hence, they should remain neutral and allow the respondent to give real answers.
Measurement errors can happen in certain samples due to confusion created by the words used in the queries, or they can happen because low–quality data is used on multi-item scales for market value surveys or for, global real estate investment, or to get the answers to changing patterns of commodity production and consumption like how many kilometres you travelled in the last year, or how many glasses of water you drink in a week, or what is your daily calorie consumption in breakfast?
Such queries can lead to proxy responses or provide irregular or general answers, increasing the chance of getting an error.
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