statistical error sample not representative too small and not random
Please answer the following:
Statistical Error: Sample not representative (too small)
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Definition: The sample drawn to represent the population is too small in comparison to the population. This is a problem because it can give an inaccurate estimate of the distribution (i.e. how the numbers vary within the population)
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What is HR Decision to be made? |
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Errors typically occur because the data used to make the decision is flawed in some way. What flawed data could lead to the error for this decision? |
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Think about what data could be used instead (to avoid the error)?
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What parameter or statistic will you use to represent the dataset?
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How would this help avoid the error? |
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Description: |
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Example: |
Statistical Error: Sample not representative (not random)
Definition: This occurs when all individuals, or instances, were not equally likely to have been selected. This results in a biased sample.
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What is HR Decision to be made? |
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Errors typically occur because the data used to make the decision is flawed in some way. What flawed data could lead to the Algorithm Aversion error for this decision? |
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Think about what data could be used instead (to avoid the error)?
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What parameter or statistic will you use to represent the dataset?
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How would this help avoid the error? |
Criteria:
– A decision is named.An actual decision must be named in the example.
-The decision is relevant to HR. The decision should be relevant to one of the functions of HR (recruiting, selection, performance management, learning & development, compensation, safety, laws & regulations, etc.) or the overall HR strategy.
–The flawed data would lead to the error. The ‘flawed’ data describe should reasonably lead to the error covered this week.
–The alternate data would minimize the error.The alternate data (data to be used instead) is distinct from the flawed data and directly addresses the error.
–The parameter or statistic is appropriate to the data.A parameter or statistic is what will be used to represent the full dataset and is based on the distribution of the data. Examples include weighted scores, means, or modes. The parameter chosen should reasonably represent the data.
–Explanation of avoiding error. The explanation of how using different data could help avoid the error should be clear and accurate.
