Sampling bias occurs when a non-representative sample is used. Should be avoided as it can bias the analysis too far in one direction
Cherry picking occurs when data is stacked to support a particular hypothesis. This could have more serious consequences in areas such as public policy, technology and healthcare.
Making metrics public is a problem because a metric becomes useless once subjects know its value.
Overfitting often occurs during the analysis process. The failure of the model can become apparent when the model is compared to future observations that are not as well fitted.
The numbers may look clean and tidy, but the underlying biases can be socially and economically turbulent.
Solution bias can be thought of as the gentler cousin of cherry-picking. With solution bias, a solution can be so cool, interesting, or elegant that it's hard not to fall in love with it.
It is crucial to convey the findings in an accessible way to people who did not participate in the project. Data scientists should feel comfortable producing elegant and compelling dashboards and graphs to ensure their findings are well communicated.

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