As the statistics students, you should know what bias in statistics is? The majority of the students still confuse about the bias in statistics. In this blog, we are going to share with you what is bias and what are its types. Let’s get started with a short introduction to bias. Bias is all about the measurement of the process. This process helps us to get over or underestimate the value of the parameter.
Bias in statistics is a term that is used to refer to any type of error that we may find when we use the statistical analyses. We can say that it is an estimator of a parameter that may not be confusing with its degree of precision. It is the tendency of statistics, that is used to overestimate or underestimate the parameter in statistics. There are several reasons to raise bias in statistics. One of the primary reasons for this is the failure to respect either the comparability or consistency.
Let A be a statistic used to estimate a parameter θ. If E(A)=θ +bias(θ)} then bias(θ)} is called the bias of the statistic A, where E(A) represents the expected value of the statistics A. If bias(θ)=0}, then E(A)=θ. So, A is an unbiased estimator of the true parameter, say θ.
The most important statistical bias types
Here are the most important types of bias in statistics. There are lots of bias in statistics. It is quite tough to cover all the types of bias in a single blog post.
Therefore I am going to share with you the top 8 types of bias in statistics. These biases usually affect most of your job as a data analyst and the data scientist. If you want to be one of them, then stay tuned with us. Let’s explore the top 8 types of bias in statistics.
When you are selecting the wrong set of data, then the selection bias occurs. It can be done as you are trying to get the sample from the subset of your audience apart from the entire set of the audience.
In this way, the calculation you may perform will not indicate or represent the whole population data. There are plenty of other reasons behind the selection bias, but the primary reason for this is, collecting the data from the easy to access source. Thus every time the data may obtain from the wrong source.
Selection bias also has the subcategory, i.e., the self-selection bias. It is just like the selection. In this, you may let the analyses subject to select themselves. Suppose that in a group of people, you allow people to choose themselves based on some criteria. In the self-selection bias, there is a possibility that lazy people may not choose themselves or considered themselves as part of the group. Because it is based on a specific behavior.
This type of bias in statistics usually occurs in interview or survey situations. As the name suggests that it is based on the memory power of the respondent. In the interview time, when the responder doesn’t remember everything correctly, then this situation emerge the recall bias.
It is the typical scenario that we remember something, and we forget something in quick sessions. Beside, it is tough for us to remember all the things we have seen, read, listen, or watched. It is usual for us, but when we do the survey, then it makes the survey an overwhelming process.
Observer bias is a pretty common bias. Because most of the time, the researcher subconsciously projecting his/her expectation from the research that it will be going to happen with this research. I mean to say that the researcher also tells others about their projection in many forms. For instance, influencing other participants, making some serious conversation. All these lead to observer bias.
When we need to perform the statistical operation on the pre-selection process. In this type of bias, the researcher focuses only on the specific part of the data rather than the entire set of data. It was also missing those data-points that are not visible anymore and also fell off during this process.
Omitted Variable Bias
Sometimes we miss the most crucial element from the model of our research. In this case, the omitted variable bias occurs. This bias leads to predictive analytics.
Cause-effect bias is one of the most critical biases for decision-makers. But most of the decision-makers are not aware of it. It is based on the simple formula that correlation does not imply causation.
The funding bias is also known as sponsorship bias. When the scientific study results are biased in favor of financial sponsor of the research, then funding bias occurs.
There are a lot more types of bias in statistics. But we have covered the most crucial one. Now it might be clear in your mind that what is bias and how it occurs in statistics.
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Any type of error in statistics that we found with the use of statistical analyses is known as bias in statistics. In other words when we want to refers any error in statistics, we call it the bias.