Do you know what bias means in statistics and what bias is? Knowing what each type of bias refers to and with what type of data is studied is proposed as essential knowledge when developing a statistical analysis. For this reason, **In this post, we explain what the types of bias in Big Data statistics are.**

## What is bias in statistics?

Bias in Big Data statistics *bias* either *biaix *in statistics) serves to indicate **the difference between the value of the expected estimator and the actual estimator** and, therefore, it is part of the properties of statistical sampling. Because of this, **There are types of bias in Big Data statistics depending on the specific nature of the values **and the treatment from which it is proposed to extract its value.

This is one of the factors that must be considered for the effective development of information processing. **Good statistical analysis** **can provide you with answers** that can complement a later, more complex Machine Learning model.

## What are the types of bias in Big Data statistics?

Since the types of bias in Big Data statistics are part of the properties of an estimator, we remind you that an estimator refers to** a statistic, that is, a function of the sample, that is used to estimate an unknown parameter of the data population**.

Now, the types of bias in Big Data statistics are the **Various ways to analyze an estimated value with the actual result**. Below, we share with you which are the main ones:

*Selection bias*

*Selection bias*

**This is a type of bias in which there is an error in the selection of elements from the data population.** For example, if you plan to establish the average height of Spanish people, but the data is collected from an unbalanced sample. In this case, **selection bias would be to go and get basketball players hoping that they are a representative sample of Spanish height**when it is obvious that it would not be assertive.

In short, this bias is a systematic error that does not depend on chance, so **It is necessary to identify it and try to neutralize its effect.**

*Survivor bias*

*Survivor bias*

Survivor bias is one of the types of bias in Big Data statistics that **is within the selection bias**that is, it also refers to a systematic error.

This type of bias occurs because **Many times the data available does not represent a reliable part of the population** that you want to measure, but a part of those who have passed certain filters.

Like, for example, pretending **study what is the academic scope of a province based on students who have already passed a large number of academic tests**without considering the population that has not been able to access any level of education.

*Variable omission bias*

*Variable omission bias*

This bias in Big Data statistics occurs when **An incorrect model is created because the most important variables have not been taken into account.**

**For example: **consider the gender gap presented in the salary.

A study carried out in 2007 in the US by the Department of Labor calculated that **The gender gap in salary between men and women was 20.4%**. That is, women on average earn 20.4% less than men. But if we take into account hidden variables such as: interruption of professional career, age, number of children, studies… **In that case, the gender gap could be between 4.8% and 7.1%.**

bias examples statistics

## Learn more about Big Data

In the development of this post, we have exposed you **What are the types of bias in Big Data statistics?**However, you have noticed that their great variety requires a much deeper knowledge of how each of them works depending on the interest of data processing. **At we encourage you to learn more about Big Data!**

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