Sampling error
Sampling error refers to the difference between a sample statistic (such as the mean or proportion) and the true population parameter. This error occurs because we are not able to sample an entire population, so we have to rely on a sample that may not perfectly represent the entire population.
For example, if we want to estimate the average income of all households in a city, we can’t survey every single household. Instead, we take a sample of households and calculate the average income based on that sample. The difference between the average income of the sample and the true average income of all households is the sampling error.
Sampling error can be reduced by increasing the sample size and using random sampling methods. However, it can never be completely eliminated, as there will always be some level of uncertainty when working with samples.
It’s important to be aware of sampling error when interpreting the results of a study or survey, as it can affect the validity of the conclusions drawn from the data.
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