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    Question

    What is the primary risk associated with bias in

    sampling, and how can it be minimized?
    A Bias can lead to false conclusions; it can be minimized by ensuring random selection. Correct Answer Incorrect Answer
    B Bias can cause data redundancy; it can be minimized by collecting data from all available sources. Correct Answer Incorrect Answer
    C Bias increases the accuracy of results; it can be minimized by using larger samples. Correct Answer Incorrect Answer
    D Bias can lead to overfitting in models; it can be minimized by applying more statistical techniques. Correct Answer Incorrect Answer
    E Bias can lead to underfitting; it can be minimized by using simpler models. Correct Answer Incorrect Answer

    Solution

    Bias in sampling occurs when certain members of the population are either overrepresented or underrepresented, leading to results that do not accurately reflect the true population characteristics. This can cause false conclusions that do not generalize to the larger population. One of the most effective ways to minimize bias is to use random sampling, where every member of the population has an equal chance of being selected. Random sampling helps to avoid the selective inclusion of certain types of individuals, thereby ensuring that the sample is representative and reducing the potential for systematic errors in the analysis. The other options are incorrect because: • Option 2 (Data redundancy) is not a direct consequence of bias in sampling. Bias impacts representation, not redundancy. • Option 3 (Accuracy) is incorrect; bias typically reduces the accuracy of results, not increases it. • Option 4 (Overfitting) and Option 5 (Underfitting) refer to issues in model development, not directly to sampling bias.

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