Here are four tips from Khan on how to do it.

10 Ways to Reduce Interviewer Bias

1. Define the job, not the person. A real job description is a list of things people need to do, not a list of things they need to have.
2. Conduct a phone screen first.
3. Use panel interviews.
4. Script the interview.
5. Don’t make snap judgements.
6. Be a juror – not a judge.
7. Use reverse logic.
8. Treat candidates as consultants.

Standardizing interview questions, keeping notes, approaching policies in an informed way, training, empathy and heightened self-awareness are few of the ways to tackle interview bias. Another tested method to reduce biases to a big extent is by automating the whole process.

1. Acknowledge you’ve got them. Khan meets people who deny they carry biases they don’t know about.
2. Learn what your biases are. Recognizing your unconscious biases is tricky by definition.
3. Ease into new waters.
4. Use tact when talking about biases with others.

There are ways, however, to try to maintain objectivity and avoid bias with qualitative data analysis:

1. Use multiple people to code the data.
2. Have participants review your results.
3. Verify with more data sources.
4. Check for alternative explanations.
5. Review findings with peers.

Strategies that might reduce recall bias include careful selection of the research questions, choosing an appropriate data collection method, studying people to study with new-onset disease or use a prospective design, which is the most appropriate way to avoid recall bias.

Bias in research can cause distorted results and wrong conclusions. Such studies can lead to unnecessary costs, wrong clinical practice and they can eventually cause some kind of harm to the patient.

Being biased is a natural tendency that we all possess but it must be reduced as much as possible to take better decisions. Bias in data analytics can be avoided by framing the right questions, which allow respondents to answer without any external influences, and by constantly improving algorithms.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others. Sampling bias limits the generalizability of findings because it is a threat to external validity, specifically population validity.

It affects the internal validity of an analysis by leading to inaccurate estimation of relationships between variables. It also can affect the external validity of an analysis because the results from a biased sample may not generalize to the population.