Bias: Concept and Classification

Statistical bias is a feature of a statistical technique in which there is a systematic deviation in the expected value of the result from the actual value. The difference between the expected value and the real value of the parameter is known as bias. The bias may show some serious problems for the researcher in the sampling procedure. In this article, we will discuss Bias, its types and classification.

Keyterms: Statistics, Bias, Statistical analysis, Measurement Bias , Non-measurement Bias, Selection Bias, Spectrum Bias, Cognitive Bias, Data-Snooping Bias, Omitted Variable Bias, Exclusion Bias, Analytical Bias, Reporting Bias, Funding Bias, Self-selection bias, Recall bias, Observer bias, Survivorship bias, Cause-effect bias.

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Bias in Statistics

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The deliberate or involuntary favouring of one class or outcome over other potential groups or outcomes in the chosen set of data is called bias. It generally defines the tendency of the measurement process. This phenomenon occurs when a model or data set is unrepresentative and it highlights some grave issues for the researcher as a simple raise cannot ease it in sample size. 

The actual variation between the expected value and the real value of the parameter considered for the experiment is usually portrayed in a bias. Multiple sources of bias are found that usually result in this anomaly and it needs to be rectified in order to provide accurate data investigation. The bias is considered to be a huge drawback in statistical analysis.

Bias

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Different Types of Bias in Statistics

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There are many important types of biases found in the field of statistics that can affect the study of a data scientist. Some of the major Bias are given below:

• Selection Bias
• Spectrum Bias
• Cognitive Bias
• Data-Snooping Bias
• Omitted Variable Bias
• Exclusion Bias
• Analytical Bias
• Reporting Bias
• Funding Bias
• Self-selection bias
• Recall bias
• Observer bias
• Survivorship bias
• Cause-effect bias

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Classification of Bias

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The bias is mainly categorized into two different categories as per the sampling method in statistics. They are-

1. Measurement Bias 
2. Non-representative Bias

1. Measurement Bias (Observation or Information Bias)

Measurement Bias takes place when major information in a survey is either measured, collected, or interpreted inaccurately. As per John’s Hopkins, it is when: “…information is collected differently between two groups, leading to an error in the conclusion of the association.” 

The main three different reasons that cause measurement bias are –

• Data Collection Error

While recording data, mishandling of data or machinery malfunction may lead to ill-handling of data by the scientist. The ineffective use of tools by researchers concerned with data collection may also result in this error.

• Leading Questions/ Fault in the Questionnaire

The interviewer may pose the question in such a way that it leads to the responses that are preferred by the researcher as compared to the opposite idea to that of the purpose of the survey. More choices can be provided in the questionnaire, for representing all the conflicting views.

• Respondents' Record-keeping System/ inadvertent false responses 

In this scenario, when many responders may have misunderstood the question and chose an incorrect option, the error or bias happens. 

For example, if the sample group is composed of numerous older adults, they might land into misunderstanding the questionnaire and fetch incorrect inputs when asked to fill the survey answers by remembering their previous experiences. It happens because of weak record keeping. The deficiency of memory becomes the cause of incorrect input in the survey.

2. Non-representative Bias (Selection Bias)

Non-representative Bias occurs because of implementing random methods during the selection process and when a survey sample fails to represent the population accurately. This inaccuracy is also referred to as selection bias. 

This type of bias happens due to involuntarily working with a specific division of population instead of the whole, leading to the unrepresentativeness of the whole population. It leads to exclusion of a specific section of the population which might skew the findings of the survey. The main reasons that cause this bias are-

• Under coverage Bias

This type of bias happens when some respondents of the sample population are not represented in the sample i.e. some members are excluded from the survey. It mainly occurs due to convenience sampling like collecting data from an easily accessible source such as a local supermarket.

• Non-response Bias

When individuals identified to represent a survey are unwilling or unable to participate in the survey, this type of bias happens. In such cases, the conflicting views of non-respondents are completely disregarded or remain unnoticed as respondents have an upper hand in the outcome of the survey.

• Voluntary Response Bias

When members of a sample are self-selected volunteers, this type of bias happens. The voluntary response may give a faulty representation of the overall population in favour of strong opinions. This also causes a lack of appropriate responses as the volunteers for the trials may not represent the targeted respondents.

• Survivorship Bias

When a lengthy process is involved for being counted as a complete response, this type of bias happens. It gives rise to biased sampling.

• Confirmation Bias

When the information pertaining to only one belief is favoured, this type of bias happens.

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Things to Remember Based on Bias

• Bais is the deliberate or involuntary favouring of one class or outcome over other potential groups or outcomes in the chosen set of data.
• Bais generally defines the tendency of the measurement process.
• The actual variation between the expected value and the real value of the parameter considered for the experiment is usually portrayed in a bias.
• Some major types of Bias are- Selection Bias, Spectrum Bias, Cognitive Bias, Data-Snooping Bias, Omitted Variable Bias, Exclusion Bias, Analytical Bias, Reporting Bias, Funding Bias, Self-selection bias, Recall bias, Observer bias, Survivorship bias and Cause-effect bias.
• Bias can be classified into two different categories- Measurement Bias and Non- representative Bias. 

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