Data analysis is a powerful tool that can uncover important insights and help make data-driven decisions. However, it's essential to consider ethical considerations when conducting data analysis to avoid bias and discrimination.

Bias occurs when a data analyst favours or discriminates against certain groups or individuals. Some ways in which bias can creep into data analysis include sampling bias, confirmation bias, and selection bias.

• Sampling bias occurs when the sample used for analysis is not representative of the larger population. This can lead to inaccurate conclusions about the population as a whole.

• Confirmation bias occurs when a data analyst looks for data that confirms their pre-existing beliefs or assumptions. This can lead to a skewed analysis that ignores important data that contradicts their beliefs.

• Selection bias occurs when the selection of data is biased towards a certain group or individual, leading to inaccurate analysis.

I stumbled upon this not too long ago; It was very enlightening to see how these biases play out when conducting data analysis and how to avoid it. I wrote more about it on my blog here.