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Common Data Analysis mistakes


Here are five things to watch out for when doing data analysis:

  1. Apples and oranges: Comparing unrelated data sets or data points and inferring relationships or similarities.

  2. Poor data hygiene: Analyzing incomplete or “dirty” data sets and making decisions based on the analysis of that data.

  3. Narrow focus/not enough data: Analyzing data sets without considering other data points that might be crucial for the analysis (for example, analyzing email click-through rate but ignoring the unsubscribe rate).

  4. Bucketing: The act of grouping data points together and treating them as one. For example, looking at visits to your website and treating unique visits and total visits as one, inflating the actual number of visitors but understating your true conversion rate.

  5. Simple mistakes and oversight: “It happens to the best of us.”

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