Gauri Shankar.

In a typical services industry scenario, reporting biases are seen across operations where depending on the metric type (HTB or LTB) we see reporting bias which would make sure that we are meeting the specified targets or getting the acceptable outcome or keeping stakeholders happy. These are typically seen where data is recorded manually and as the input for analysis is a biased set of data, the outcome of the analysis is ought to be faulty. The best way to negate reporting bias is to automate the reports wherever it is feasible, and having measures to check the sanctity of the data if manual intervention is unavoidable. Another example would be "exit polls", where if a certain demography of section is avoided to showcase the outcome is a classic case of reporting bias.