Data analysts can take various measures at each stage of the process to reduce the impact of statistical bias in their work.
Understanding the source of statistical bias can help to assess whether the observed results are close to actuality.
[1] Statistical bias can have significant real world implications as data is used to inform decision making across a wide variety of processes in society.
Data is used to inform lawmaking, industry regulation, corporate marketing and distribution tactics, and institutional policies in organizations and workplaces.
Therefore, there can be significant implications if statistical bias is not accounted for and controlled.
For example, if a pharmaceutical company wishes to explore the effect of a medication on the common cold but the data sample only includes men, any conclusions made from that data will be biased towards how the medication affects men rather than people in general.
That means the information would be incomplete and not useful for deciding if the medication is ready for release in the general public.
Bias implies that the data selection may have been skewed by the collection criteria.
One may have a poorly designed sample, an inaccurate measurement device, and typos in recording data simultaneously.
If someone receives a ticket with an average driving speed of 7 km/h, the decision maker has committed a Type I error.
In other words, the average driving speed meets the null hypothesis but is rejected.
On the contrary, Type II error happens when the null hypothesis is not correct but is accepted.
Bias in hypothesis testing occurs when the power (the complement of the type II error rate) at some alternative is lower than the supremum of the Type I error rate (which is usually the significance level,
All types of bias mentioned above have corresponding measures which can be taken to reduce or eliminate their impacts.
Bias should be accounted for at every step of the data collection process, beginning with clearly defined research parameters and consideration of the team who will be conducting the research.
[2] Observer bias may be reduced by implementing a blind or double-blind technique.
Avoidance of p-hacking is essential to the process of accurate data collection.
[17] Careful use of language in reporting can reduce misleading phrases, such as discussion of a result "approaching" statistical significant as compared to actually achieving it.