In statistics, error refers to the difference between an observed or measured value and the true or actual value. It represents the amount of uncertainty or inaccuracy in a measurement.
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Bias: Bias refers to a consistent deviation from the true value in one direction. It can lead to systematic errors in data analysis.
Residuals: Residuals are the differences between observed values and predicted values in regression analysis. They represent individual errors for each data point.
Sampling Error: Sampling error occurs when a sample is used to estimate characteristics of a larger population. It is caused by random variation and can affect the accuracy of statistical inference.