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Provide a Human Resource decision that has to be made as an example of the following statistical errors: expectations of success, stereotype threat, ignoring context and team interdependence

Provide a Human Resource decision that has to be made as an example of the following statistical errors: expectations of success, stereotype threat, ignoring context and team interdependence

Statistical Error: Expectations of success

Definition: The assumption, prediction, and expectation of a behavior, performance, or action that elicits a positive ability or outcome of an individual or group.

1. Provide a Human Resource decision that has to be made as an example to the statistical error expectations of success. Definition is provided above, give an example/scenario.

2. 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?

3. Think about what data could be used instead (to avoid expectations of success)?

4. What parameter or statistic will you use to represent the dataset?

5. How could this help avoid the error?

Statistical Error: Stereotype threat

Definition: Follows from expectations of success. It is a situation in which people are or feel themselves to be at risk of conforming to stereotypes about their social group.

1. Provide a Human Resource decision that has to be made as an example to the statistical error stereotype threat. Definition is provided above, give an example/scenario.

2. 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?

3. Think about what data could be used instead (to avoid stereotype threat)?

4. What parameter or statistic will you use to represent the dataset?

5. How could this help avoid the error?

Statistical Error: Ignoring context

Definition: The belief that performance is determined by the individual effort and neglects the tools, location, colleagues, and environment that allows someone to successfully perform.

1. Provide a Human Resource decision that has to be made as an example to the statistical error ignoring context. Definition is provided above, give an example/scenario.

2. 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?

3. Think about what data could be used instead (to avoid ignoring context)?

4. What parameter or statistic will you use to represent the dataset?

5. How could this help avoid the error?

Statistical Error: Team interdependence

Definition: This captures the relationship between individual performance and team performance. When team interdependence is high, individual performance is strongly impacted by the performance of the members of the team.

1. Provide a Human Resource decision that has to be made as an example to the statistical error team interdependence. Definition is provided above, give an example/scenario.

2. 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?

3. Think about what data could be used instead (to avoid team interdependence)?

4. What parameter or statistic will you use to represent the dataset?

5. How could 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