Types of Bias in Statistics and the Affect Data Bias Has on Your Business

Statistical bias is a term used to describe statistics that don’t provide an accurate representation of the population. Some data is flawed because the sample of people it surveys doesn’t accurately represent the population. Other data may be flawed because too many variables were omitted, which can affect the accuracy of the data in the end. Let’s take a PC for example. You might know you’re purchasing a new PC with an Intel processor, but there are so many variables left out that you can’t determine whether or not that PC is a good deal. You would also have to know about the graphics card, the RAM, the storage capacity, and more.

Understanding statistical biases is especially important if you’re running an e-commerce business because it can skew data and negatively impact your decision-making process.

There are several types of bias in statistics, and avoiding and understanding these statistical biases can help you better interpret data. Here are some of the different types of statistical biases you may encounter.

Confirmation bias

Confirmation bias is an error that involves allowing a preconceived notion to impact how you prioritize or interpret information. An example of confirmation bias would be if you had a strong opinion that most people preferred vanilla ice cream over chocolate ice cream and, as a result, gave more weight to data that supported that conclusion.

Selection bias

Selection bias is an error that stems from using population samples that don’t accurately represent the entire target group. For example, data taken from one neighborhood would not accurately represent a large city. There are many reasons selection bias arises—some intentional, some not—including voluntary participation, limiting factors for participation, or insufficient sample size.

Outlier bias

Outliers can significantly skew data. For example, when analyzing income in the United States, there are a few extremely wealthy individuals whose income can warp any calculation of averages. For this reason, a median value is often a more accurate representation of the larger population.

Observer bias

Observer bias is a type of statistical bias that’s biased as a result of the subjectivity of the observer. No human can be completely unbiased, so observer bias is always going to be an issue. The best you can do is learn to recognize it.

An example of this was a rat test performed in the 1960s where two groups of students tested rats, which were categorized as “bright” and “dull”. The students who had the “dull” rats handled them poorly and reduced their chances of completing the maze, which ultimately affected the results of the study.

Funding bias

Funding bias refers to the likelihood that a study has to favor the person who funded it. These studies tend to provide inaccurate data that can make it difficult to apply that data to your business.

Funding bias is especially popular with product comparisons. If Bounty pays for a paper towel comparison, that comparison is much more likely to favor Bounty than another brand.

Omitted variable bias

With omitted variable bias, the lack of a variable affects the legitimacy of the statistic. For example, a study about cars that doesn’t include the year or mileage may provide inaccurate results.

Omitted variable bias is one of the most common types of bias in statistics. When you’re looking at data, make sure that data takes into account all the relevant variables.

Survivorship bias

Survivorship bias is when you only take into account surviving data points. By not taking into account every potential source of data, you could be getting a flawed representation of the data.

A classic example of survivorship bias is WWII, when planes that survived were studied so they could be reinforced where they were shot most. In reality, it would have been best to look at downed planes and reinforce future models in the spots where those planes were shot and taken down.

How human bias influences data

Algorithms built to mimic the process of learning and conclusion-making do so by processing data gathered from human users. Massive amounts of data are processed to identify patterns, which algorithms can then use to do things like identify common preferences or even mimic human behaviors. These algorithms have a wide range of applications for companies, from lead generation based on targeted marketing to more sophisticated artificial intelligence operations.

Bias is a component of the human thought process, and data collected from humans therefore inherently reflects that bias. This makes it incredibly difficult to gather and adjust data so that it omits bias while retaining its accuracy—especially since the determination of what is a bias is often subjective.

Ethics in data collection

Ethical matters regarding the collection of data are increasingly being raised by the public, especially as it concerns consumer privacy. While consumer data is used by CRM systems and similar technology to improve customer experience, companies can also use, buy, or sell such data in ways that bump up against the edge of what’s legal or ethical, eroding consumer trust across the board.

In fact, there is such widespread concern that many laws and regulations have been enacted on the subject across the globe, such as the European Union’s General Data Protection Regulation (GDPR). Those who want to work ethically with mined consumer data may find it helpful to seek out businesses that are compliant with GDPR and/or similar codes.

Data bias in AI

The impact of biased data on applications such as artificial intelligence is not always theoretical, or even subtle. A famous example is Microsoft’s Tay. Tay was a chatbot released by Microsoft in 2016 that used AI technology to create and post to Twitter. Soon after going live, Tay began tweeting concerning content, much of it discriminatory in nature.

After deactivating Tay, the Microsoft team released a statement about the incident. This statement pointed to Twitter users intentionally spamming Tay’s conversational threads with inflammatory statements as the source of its behavior. Tay used those threads as a means of data mining to influence its output. Although this incident was at least partially caused by intentional sabotage from users, it illustrates how discrimination can take form in the data that is increasingly being put to work in our day-to-day lives.

Businesses use data for everything in the digital age, so the different statistical bias types can have a major impact on your business. Understanding statistical bias can help you avoid mistakes and get the most out of the data you collect for your company.

When you’re making changes to products, services, or marketing efforts based on data, you need to make sure that data is accurate. Looking for and actively working around the types of bias in statistics can help with that.

Types of bias in statistics: FAQs

What is meant by statistical bias?

Statistical bias is a term used to refer to stats that aren’t necessarily accurate as a result of some kind of issue with the data. This could be a variable that was left out, observer bias, or funding bias where one company paid for the study. As a small business owner, understanding and compensating for statistical bias is an important part of e-commerce marketing.

What are examples of bias in statistics?

Omitted variable bias is one of the most common examples of bias in statistics. You can probably think of some data you’ve seen that was later invalidated because it “didn’t take something into account.” For example, you can’t simply look at loading times and other website performance metrics without considering differences in hardware, location, and more. Funding bias is also common, especially in cases where a brand pays for a product comparison.

What kind of bias are there in statistics?

There are several types of bias in statistics, including confirmation bias, selection bias, outlier bias, funding bias, omitted variable bias, and survivorship bias. You should understand the different types of bias in statistics and how they can affect your business.

Take advantage of your data by understanding statistical bias

Understanding statistical bias helps you make the most out of data so you can make better decisions for your business. From outlier bias to survivorship bias, there are a variety of different types of statistical biases you need to be aware of for the sake of your business.

You can use Mailchimp to help with everything from analyzing data to managing marketing campaigns, so you can help your business succeed, without any statistical bias getting in the way.