When considering statistical inference, it is important to ensure that the sample used is generalizable to the actual population being estimated. Sampling errors result may create biases that cause the actual error term to be much larger than the standard error. This, in turn, may lead to Type I and Type II errors even though the critical test statistics and p-values are outside the rejection region. Define and discuss non-response bias, undercoverage, and response bias. Identify specific scenarios in business research that could lead to these types of biases. Finally, explain how to prevent them from affecting the data collection.
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To recieve any credit for the discussion, all discussion posts must reference at least 2 peer-reviewed journal articles published within the past 5 years.
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