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Last updated on Nov 8, 2024

You're facing client concerns about statistical outliers. How can you ensure the validity of your findings?

When clients question outliers, ensure your data's integrity with clear strategies. To navigate this challenge:

Explain the process: Detail how you identify and manage outliers in your analysis.

Provide context: Help clients understand why certain data points are outliers.

Reassure with robustness checks: Show how your conclusions hold up even when outliers are excluded.

How do you handle outlier-related queries from clients? Share your strategies.

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Last updated on Nov 8, 2024

You're facing client concerns about statistical outliers. How can you ensure the validity of your findings?

When clients question outliers, ensure your data's integrity with clear strategies. To navigate this challenge:

Explain the process: Detail how you identify and manage outliers in your analysis.

Provide context: Help clients understand why certain data points are outliers.

Reassure with robustness checks: Show how your conclusions hold up even when outliers are excluded.

How do you handle outlier-related queries from clients? Share your strategies.

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1 answer

Anuj Talwar

Economics of Inequality and Climate Change | Ex - WRI, GBL, EPIC (India)
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It's crucial to understand that outliers represent occurrences or instances that are not 'expected', given historical data and circumstances. Therefore, the treatment of outliers should be well-outlined for the client at their level of technical expertise. It may be useful to explain to them that outliers could arise as errors in measurement and/or design. The analyst should carefully compare the effects of including versus excluding the outliers from analysis.

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