Humans are inherently biased, so a successful business analyst should be able to minimize bias while working with data. Cognitive biases play a significant (though often unacknowledged) role in how we understand the world around us and are present when analysing data.
In your initial post, address the following:
From the video resource discussing twelve cognitive biases, what are two or three biases you may be prone to in your analyses for this course?
How are they likely to affect your analyses and recommendations?
How can this impact how you present your findings to your manager?
Is it enough to simply show the numbers or a graph and expect the reader to glean the same meaning as you have? Explain.
How might you compensate for these biases in how you explain the results of your analysis or how you use or don't use graphs and other visuals?
Full Answer Section
These biases are likely to affect my analyses and recommendations by leading to inaccurate or incomplete conclusions. For example, if I am biased towards confirming my existing beliefs, I may be less likely to identify patterns or trends in the data that contradict my expectations. This could lead to me making recommendations that are not supported by the evidence.
To compensate for these biases, I will need to be more mindful of my own thinking process and actively seek out information that contradicts my existing beliefs. I will also need to be careful not to overinterpret data and to clearly distinguish between facts and inferences.
In presenting my findings to my manager, I will need to be clear about the limitations of my analysis and to avoid making any claims that I cannot support with evidence. I will also need to be transparent about the methods I used and the assumptions I made.
Simply showing the numbers or a graph is not enough to expect the reader to glean the same meaning as I have. Graphs and other visuals can be helpful in summarizing data and highlighting important trends, but they are not always a substitute for a clear and concise explanation.
To effectively communicate my findings, I will need to provide a narrative that explains what the data means and how it relates to the business problem at hand. I will also need to use plain language and avoid jargon that may not be understood by my audience.