(1) It is nearly impossible to be unbiased. But it is one of the most noble aspirations and bias mitigation techniques can be developed and practiced, both generally, as well as to address specific bias. (3) Cognitive Bias is unfair prejudice against anyone or anything for any reason.

How to avoid the self serving bias?

1. Give others credit during success. Every time you succeed, try to find 5 people or reasons behind the victory.
2. Find an area for improvement for any bad outcome.
3. Give yourself extra time to evaluate the outcome.

Bias against yourself can take many forms including: not taking up opportunities, putting things off or not facing up to problems. Bias is keeping us from speaking up, seeking feedback or from being more creative. It can shape our attitudes toward our colleagues in ways that can undermine our own credibility.

Bias can damage research, if the researcher chooses to allow his bias to distort the measurements and observations or their interpretation. When faculty are biased about individual students in their courses, they may grade some students more or less favorably than others, which is not fair to any of the students.

Bias tests aim to measure the strength of association between groups and evaluations or stereotypes. The outcomes of these bias tests can provide a clearer picture of how people perceive those in their outer group. Helping people become aware of their biases is the first step to addressing them.

“In reality, bias can be bad or wrong … but it can also be tremendously helpful.” One example of an instance in which bias is helpful, said Ross, is when someone is approached by a stranger with a knife in their hand.

The internal validity, i.e. the characteristic of a clinical study to produce valid results, can be affected by random and systematic (bias) errors. Bias cannot be minimised by increasing the sample size. Most violations of internal validity can be attributed to selection bias, information bias or confounding.

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.

Biases when interacting with participants They result from verbal and non-verbal cues and gestures that influence the participant’s thinking or behaviour during the experiment, and become damaging when they systematically ‘lean’ towards one particular outcome.

Attrition occurs when participants leave during a study. It almost always happens to some extent. Systematic differences between people who leave the study and those who continue can introduce bias into a study’s results – this is attrition bias.

The term attrition refers to a gradual but deliberate reduction in staff numbers that occurs as employees retire or resign and are not replaced. Attrition happens for several reasons, including pay, lack of growth, and poor workplace conditions.

The following measures may help to lessen the effects of (or prevent entirely) loss of data from attrition:

1. Create a project identity,
2. Keep follow-up interviews as brief as possible,
3. Offer incentives (e.g. cash),
4. Use a good tracking system with detailed contact information,

We can assess whether these outcomes may be affected by attrition bias by comparing rates of loss to follow-up between the arms of the trial as well as by examining the baseline characteristics of participants who were lost to follow-up and the characteristics of those remaining.

Because the success of your company may rely on it, we’ve put together a list of 10 steps to help reduce call center agent attrition.

1. Optimize recruiting and hiring.
2. Enhance training programs.
3. Enhance customer satisfaction focused monitoring.
4. Establish clear communication channels.
5. Enhance recognition and rewards programs.

Selection bias due to loss to follow up is the absolute or relative bias that arises from how participants are selected out of a given risk set 3. Here and throughout this paper, absolute bias refers to bias of an absolute measure, while relative bias pertains to the bias of a relative effect measure.

Performance bias is specific to differences that occur due to knowledge of interventions allocation, in either the researcher or the participant. This results in differences in the care received by the intervention and control groups in a trial other than the intervention that are being compared.

Blinding (sometimes called masking) is used to try to eliminate such bias. It is a tenet of randomised controlled trials that the treatment allocation for each patient is not revealed until the patient has irrevocably been entered into the trial, to avoid selection bias.

Bias in clinical trials may be described as systematic errors that encourage one outcome over others. The potential effect of bias is that investigators will come to the wrong conclusions about the beneficial and harmful effects of interventions.

Surveillance bias is a type of information bias which occurs when one group of subjects is followed up more closely than others, for example, if they undergo medical treatment1, 2.

Missing data can be a major cause of information bias, where certain groups of people are more likely to have missing data. An example where differential recording may occur is in smoking data within medical records. The bias was more likely when the exposure is dichotomized.

Prevalence-incidence bias is a type of selection bias. It is also known as “Neyman bias”. Prevalence-incidence bias occurs when individuals with severe or mild disease are excluded, resulting in an error in the estimated association between an exposure and an outcome.

Volunteer bias is systematic error due to differences between those who choose to participate in studies and those who do not.