Bias

A bias is the deliberate or involuntary favouring of one class or outcome over other potential groups or outcomes in the chosen set of data. If you are asked to define bias in statistics- it is a phenomenon that occurs when a model or data set is unrepresentative. This sampling procedure highlights some grave issues for the researcher as a simple raise cannot ease it in sample size. Bias portrays the actual variation between the expected value and the real value of the parameter considered for the assay. There are multiple sources of bias that result in this. It is a drawback in statistical analysis and needs to be rectified in order to provide accurate data investigation. 

In this article, all types of bias have been discussed in detail to help you identify potential sources of bias while planning a sample survey. On identifying a probable bias, it is important to determine whether the result is an overestimate or an underestimate.

A statistical term that means a systematic deviation from the actual value. Bias  a sampling procedure which may show certain issues for a researcher, since  a mere increase cannot reduce it in sample size. The difference between the expected value and the real value of the parameter is what Bias is. Bias can be described as a kind of phenomenon which can occur when any model or any data is unresponsive.

Bias in Statistics can be of many types and is classified into two parts, the Measurement Bias and The Non Representative Sampling Bias.

Different Types of Bias in Statistics

The major types of bias that can significantly affect the job of a data scientist or analyst are:

1. Selection bias

2. Self-selection bias

3. Recall bias

4. Observer bias

5. Survivorship bias

6. Omitted variable bias

7. Cause-effect bias

8. Funding bias

9. Cognitive bias

10. Spectrum Bias

11. Data-Snooping Bias

12. Omitted-Variable Bias

13. Exclusion Bias

14. Analytical Bias

15. Reporting Bias

As per the sampling method in statistics, bias can be critically segregated into two major classifications:

1.  Measurement Bias. 

This takes place for the entire duration when carrying out a survey, and the reasons for its consequences can be said to be because of the following;

1. The Error Takes Place Only When Recording Data.

When recording any data, we get errors because of the malfunction of the instruments used for data collection,  or, also due to the ineffective handling of the tools by the concerned data collection people.

2. Leading Questions for survey.

Preparations of the questions that are required for the survey might be put in a manner  which is interviewer -friendly,  answers will be according to the interests of the interviewer, questions that will  be answered which are preferred by the interviewer/researcher. There should be more choices for them to get a proper report.

3. False Responses from Respondents.

Situations can arise  when many responders misunderstand the questions and give an incorrect option.

In the care of slightly older respondents, when they are expected to fill the survey answers by remembering their previous experiences, this may cause further misunderstanding and this could fetch incorrect inputs due to weak record keeping.

2. Non-Representative Bias (Selection Bias): 

This happens when a survey sample represents the population inaccurately, which is due to working involuntarily with only a specific division of population and here the sample becomes unrepresentative of the whole population. 

The major types of selection bias are:

1. Undercoverage Bias which occurs when some respondents of the sample population are not wholly represented. The reason behind such a bias is the convenience of sampling, which takes place when the data is collected from an easily accessible source. Example can be the local supermarket.

2. Non-response Bias, occurs when the individuals who are  identified to represent a survey are unwilling or unable to participate in the survey. In this case, the respondents have an upper hand regarding the survey’s outcome. 

3. Voluntary response  bias occurs when members who take samples are the self-selected volunteers. For example, the call-in radio shows. These Responses  give a faulty and wrong  representation of the overall population who are in favor of strong opinions.

4. Volunteer Bias in statistics can be described by the situation where the population that volunteers for the trials may not represent the targeted respondents.

5. Survivorship Bias refers to that type of survey  which calls for the survival of a lengthy process for being counted as a complete response that  gives rise to biased sampling.

All information that defines bias in statistics is included in this article with special focus on different kinds of bias, leading to a clear idea about identification as well as rectification of bias in data analysis.

Did You Know?

An estimator in statistics is a set of protocols for estimating a quantity based on collected data. A biased estimator is the one that gives a false reflection of the population parameter. Suppose you are in a party, playing the game of “bell the cat” where you get to stick the bell to the cat’s picture while being blindfolded. The person, who pins the bell closest to where the bell should go on the neck, wins the game. But unfortunately, even after trying ten times, you tend to put the bell either on the nose or the stomach or the ears of the cat. In this case, your estimation about the location of the exact position of where the bell must be pinned to is a biased estimator.