Bias Definition In Statistics from Vedantu
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:
Selection bias
Self-selection bias
Recall bias
Observer bias
Survivorship bias
Omitted variable bias
Cause-effect bias
Funding bias
Cognitive bias
Spectrum Bias
Data-Snooping Bias
Omitted-Variable Bias
Exclusion Bias
Analytical Bias
Reporting Bias
As per the sampling method in statistics, bias can be critically segregated into two major classifications:
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;
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.
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.
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.
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:
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.
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.
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.
Volunteer Bias in statistics can be described by the situation where the population that volunteers for the trials may not represent the targeted respondents.
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.
FAQs on Bias
1. What is bias in the context of statistics?
In statistics, bias refers to a systematic error that causes a difference between the results of a study and the true value of the population parameter being estimated. It is not a random error but a consistent tendency to either overestimate or underestimate a value, leading to inaccurate conclusions. This phenomenon occurs when the data collection process or the analytical model is unrepresentative of the true population.
2. What are the two primary categories of bias found in statistical surveys?
Statistical bias is broadly classified into two main categories based on the source of the error:
- Measurement Bias: This occurs during the data collection phase when the method of measuring or observing is flawed. It can be caused by faulty instruments, leading questions in a survey, or respondents providing inaccurate information.
- Selection Bias (or Non-Representative Bias): This arises when the sample selected for the study is not representative of the larger population. As a result, the findings from the sample cannot be reliably generalised to the entire population.
3. Can you provide a real-world example of selection bias?
A classic example of selection bias is voluntary response bias. Imagine a news website posts a poll asking, "Is the city's new metro system effective?" Only people who feel very strongly about the topic (either positively or negatively) are likely to participate. This sample is not representative of the entire city's population, as it excludes those with neutral or mild opinions. Therefore, the poll results would be biased and not reflect the true public sentiment.
4. What is the crucial difference between a simple statistical error and bias?
The key difference lies in their nature and effect. A statistical error is typically random and unpredictable. With a large enough sample, random errors tend to cancel each other out. In contrast, bias is a systematic error that consistently skews the results in a single direction. It is a flaw in the study's design or methodology and cannot be reduced simply by increasing the sample size.
5. What is survivorship bias and how can it lead to incorrect conclusions?
Survivorship bias is a logical error that occurs when focusing only on the subjects or examples that have 'survived' a certain process, while overlooking those that did not. For example, if you study the business strategies of only successful companies, you might conclude that taking huge risks is a key to success. This conclusion is biased because it ignores the many companies that took similar risks and failed, and are therefore not part of the 'surviving' sample.
6. How can an interviewer's actions introduce bias into a research survey?
An interviewer can unintentionally introduce interviewer bias in several ways. This includes asking leading questions that suggest a desired answer, using a tone of voice that influences the respondent's choice, or reacting differently (e.g., nodding in approval or frowning) to certain answers. These subtle cues can push respondents to give socially desirable or interviewer-pleasing answers rather than their true opinions, thus skewing the data.
7. Why is it so important for a researcher to identify and minimise bias in a study?
Identifying and minimising bias is critical because its presence can completely invalidate the results of a study. Biased data leads to inaccurate conclusions and flawed decision-making. If a statistical analysis is meant to reflect a real-world situation, such as the effectiveness of a new drug or a public policy, biased results can have serious negative consequences, leading to wasted resources or harmful outcomes.
8. Will increasing the sample size of a study help in reducing bias? Why or why not?
No, simply increasing the sample size will not reduce or eliminate bias. A larger sample size can reduce the impact of random error, making the estimate more precise. However, if the data collection method is systematically flawed (e.g., using a biased survey question or sampling from the wrong population), a larger sample will only produce a more precise but still inaccurate result. To fix bias, you must correct the underlying flaw in the methodology.
9. How does omitted-variable bias create misleading results in a statistical model?
Omitted-variable bias occurs when a statistical model fails to account for a relevant variable that is correlated with both an independent variable and the dependent variable. This omission can create a spurious or misleading relationship. For instance, a study might find a strong correlation between ice cream sales and crime rates. However, the omitted variable is temperature. Hot weather independently causes both ice cream sales and crime rates to rise. Without accounting for temperature, one might wrongly conclude that eating ice cream causes crime.
