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The Bias in Data: Why Should We Care?


In this world of big data, it is essential to make sure that our outcomes and the result of data research are free of any bias. The sole responsibility of a data analyst is not only to prevent bias but also to utilize it most effectively. In other words, it was long ago when software mastered the art of solving Rubik’s cube. With the help of this unbiased data collected, robots developed are efficiently programmed to perform several tasks.

You might be thinking, why is it important to ponder on this topic?


If you think that biases always negatively impact the organization, you are wrong. You must know that bias helps an organization to allow down the focus, and they can derive relevant and exact information with its help.

Unbiased data can help your organization to answer some of the questions that you didn’t even ask. For example, you can even know which salesman was the best performer or which day is the best to sell your product.

Well, the data has not been too important in the last few eras. But, Big data is here to stay, and we are already living in an era that is dependent on data analysis and collection. This data further helps the integration of Artificial Intelligence in systems that help to complete tasks efficiently without the interference of a human.

Why should we care about the bias in data?

Predictive models only see the reality of the world through the data they get. They know of no other model other than the data model. It is very important to remove biases, as the biased models can limit and reduce the credibility of the stakeholders. In addition to this, biased models can also lead to discrimination among some groups and classes of people. Due to all these problems and issues, it is very important for the data scientist to pay attention that they don’t get the bias in data.

Types of bias in data:

There are five main types of bias in data:

Confirmation bias:

This bias arises when a person who is conducting a data analysis already wants to prove any assumption that he/she may have.

Selection bias:

When the data is selected selectively, this bias may arise. In this case, the sample taken does not represent the population completely.

Outlier:

Outlier means the data selected that is too high or too low. It means extreme samples.

Outfitting an underfitting:

When the model of prediction gives an oversimplified model of reality, it is called underfitting. When the model is too complicated, it is called outfitting.


Confounding:

When a variable is totally outside the scope of existing models, it is called by the name confounding model.

How to avoid the bias in data?

The qualitative characteristics of data make it too difficult for the data analyst to completely separate themselves from the data collected. But there are different ways that can be used to avoid the bias in data. Some of the ways include:

· Use different people from different teams and backgrounds to code the data to avoid any biases.

· Let the participants in the research review your result

· Try to verify your result with more data sources

· Look for alternative explanations

· Review your results with your friends and peers.

When the process of data collection and analysis is playing a major role, it is very important for everyone involved in the process to make sure that the data is completely unbiased. Let the data collection process and data analysis process be “statistics” and try to avoid lies and biases as much as possible. After all, the world is running on data today!!

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