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Sampling errors occur when the sample chosen from the population doesn’t perfectly represent the entire population. This type of error is due to random variation and chance.
Whenever you take a sample from a larger group, it’s unlikely to perfectly match the entire population’s actual parameters. The differences between the sample and the population are what we call sampling errors.
Imagine taking two different samples from the same population. Both will likely have different means because of the randomness involved. This doesn’t result from any mistakes. It's just a natural part of the sampling process.
For example, if you survey a small group to estimate the average height of people in a city, the results will vary each time you take a new sample.

Nonsampling errors are all the other kinds of mistakes that can happen during a survey, from beginning to end. They’re different from sampling errors because they don’t result from the act of sampling itself.
These errors can happen because of human mistakes, incorrect data collection methods, or even poor study design.
For instance, if a questionnaire asks confusing questions, respondents might not understand and give incorrect answers. This would introduce a nonsampling error.

• Measurement errors
• Data recording errors
• Bias from poorly designed questionnaires
• Nonresponse errors
• Processing errors

All of these fall under nonsampling errors and can be minimized with careful survey design and execution.

Survey process errors happen at various stages throughout the survey. These errors aren't related to how the sample is drawn.
They stem from issues in how the survey is conducted, from the initial design to the final data analysis. These errors include:

• Designing flawed questionnaires
• Training interviewers poorly
• Using inappropriate data collection methods

For example, if interviewers are not well-trained, they might misinterpret responses or enter data wrongly, leading to inaccuracies in the results.
It's crucial to have a well-thought-out process to avoid these errors and ensure the survey's accuracy.

Data collection errors are specific types of nonsampling errors that occur during the actual gathering of data. These errors can significantly affect your survey results.
These errors can arise from different sources:

• Respondents' misunderstanding questions
• Interviewers recording responses incorrectly
• Technical failures during data collection

For instance, if a respondent misreads or misinterprets a survey question and gives a wrong answer, this is a data collection error. Proper training for interviewers and clear questions can help minimize these mistakes.

The size of your sample can significantly impact the amount and type of errors in your survey.
Larger samples tend to have smaller sampling errors because they better represent the population.
However, increasing the sample size alone won't reduce nonsampling errors. To minimize nonsampling errors, you need good study design and execution practices.
It's important to balance the sample size to manage both types of errors efficiently.
While larger samples improve the accuracy of the results through reduced sampling error, attention to detail in the survey process and data collection will help keep nonsampling errors at bay.