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Mathematical Definition of Estimator Bias
, where is the estimator of a parameter, is the true value of that parameter, and is the sample size/number of data points. The bias of an estimator for a parameter is the difference between the expected value that the estimator will take when trained on data and the actual value of the parameter used to generate the data.
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A data scientist is trying to estimate the typical number of daily active users for a new mobile app. They collect data for the first five days: [100, 150, 120, 180, 1000]. The last day was a special promotional event. They consider two different functions to produce a single-value estimate from this data:
- Function A: Calculates the arithmetic average of the five data points.
- Function B: Identifies the middle value after sorting the data points.
Which of the following statements best analyzes the difference between the estimates produced by these two functions in this specific context?
Consider a function designed to produce a single-number estimate of a population's central value from a sample of data. The function works by taking only the minimum and maximum values from the sample and calculating their average. This function is a robust choice for this task, particularly in situations where the sample might contain extreme outliers.
Mathematical Definition of Estimator Bias