The Shocking Ways Data Bias Makes Women ‘Irrelevant,’ and What We Can Do to Stop It

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Women are still seen as secondary and you may not even know how this is happening. Some are obvious: women earn less than men and they have fewer leadership positions in organizations and boards. Women’s career paths have also been significantly diverted (or terminated) as a result of the pandemic on a scale not seen in men’s career paths. Women’s perspectives and experiences are downplayed, contributing to gender bias and gender disparity. Their needs are taken for granted and/or thrown into the bubble of male needs or desires.

A lesser-known way these differences occur is through dates. You might say, “How can that be? Data is based on research and facts. How can this data actually be distorted? creates?The answer involves a disregard for women in research – from business to technology to medicine and other practical aspects of life.

What is data distortion?

Data is collected to prove what works (or doesn’t work) for different projects, concepts or innovations. It allows researchers to know what needs to be adjusted and/or pushed forward. One variable within research that makes investigation challenging is completeness: all aspects of data use must be considered. If an aspect is overlooked, negative or dangerous results can result. For example, consider the case of a self-driving Uber car beat and killed a woman in Arizona. Uber found that the car couldn’t tell an object was a pedestrian unless it was near a crosswalk — an oversight that resulted in dangerously skewed data.

Also consider facial recognition software, a tool used by many law enforcement agencies. After it was used to identify, target, and convict criminals, research found that its algorithms were significantly less accurate when it came to many cases people of color and women. The resulting bias had far-reaching implications for both civil rights and public safety.

In designing research and data collection, researchers rely on a sample population intended to represent the larger population to which the research and other data will be applied. But to be effective, this sample must include all sectors of the larger population. When it comes to gender bias in data, women are overlooked.

Related: How entrepreneurs can conduct primary market research

What is gender data bias?

In many research areas, women are not or only insufficiently included in the sample population. This might be fine if the data weren’t applied to women, but it is. Products, services and strategies are generalized for women when the research behind them is not based on data include Women. Read that again: There are things that are made and used by women and we don’t know if they work or are safe for them. Consider these examples:


Women’s hands are typically smaller than men’s hands (about 1 to 2 inches), but this is not usually taken into account when designing mobile phones. These now virtually mandatory tools – and especially a trend towards their increasing size – don’t take into account how a phone fits into smaller hands. While some companies offer smaller models, they tend to be less powerful or offer fewer options. Another example concerns Google Home: according to a study by sociolinguist and data scientist Dr. Rachael Tatman in 2016, the speech recognition databases used to develop this application were male-dominated, making them 70% more likely to be recognized and responded to effectively. Male voices over female voices.


Women are more likely to die from heart attacks because their symptoms are often considered “atypical.” This is because standard symptoms were identified based on research that focused on males’ presentations (chest pain, left arm pain) versus females (shortness of breath, nausea, fatigue, abdominal pain). Collectively, this bias is often referred to as Yentyl Syndrome and is detailed in an article from 2011 in which European Heart Journal. This makes the male body the standard for medical understanding, and the same is true for medical research, where according to a 2011 about 85% of the rodents used in tests are male Neuroscience and biobehaviour reviews article.


Women are more likely to be seriously injured in car accidents. Why? Because car manufacturers have a “standard seating position” in safety research that is based on male dimensions. Females are usually smaller than males and therefore need to sit higher and closer to the steering wheel to see clearly, but this information is not included in manufacturers’ “standard”. Women are also more likely to die in a car accident due to similar gender data bias. Male crash test dummies are typically used for driver seat testing. When female crash test dummies are used, they are usually confined to the passenger seat. The result is that the existing research on female drivers is neither accurate nor applicable.


In some countries, female officers wear vests designed and researched on male bodies, making them more vulnerable and less protected than their male counterparts. A trade union congress 2017 report details these differences in UK PPE application, and many of the same issues apply equally in the United States.

Related: Labeled women and prejudice in the workplace

office climate

Research published by nature climate change in 2015 revealed how workplace temperature affects productivity and also demonstrates a gender bias in the data. If you’re a woman and wonder why you’re always cold at work while your male colleagues are comfortable, it’s because physiological differences aren’t taken into account when researching the ideal temperature for employee productivity and well-being. Research tends to use male physiology as the standard, which does not account for gender differences in body mass index or overall body structure. And from a dressmaking standpoint, men tend to wear more suits and layers as part of typical office attire, while women don’t. Without being taken into account in the context of office climate research, women are disadvantaged.

urban planning

This field of study and its application is typically male-dominated and too often limited to their perspectives: data on women is largely ignored. For example, subways are built with efficiency and affordability in mind, and away from monitored areas, dim lighting and unoccupied areas are more common. This puts women at a significant disadvantage and creates spaces where attacks and/or harassment are more likely. Additionally, suburbs are still being designed with an outdated perspective of the male as breadwinner, emphasizing daily commute efficiency (according to a 2021 Our Secure Future article). This makes managing domestic life (including errands and childcare) more difficult, and this vital task is often still the responsibility of women.

How, you might ask, does this rampant disregard for data in so many areas of life come about? The answer is simple, and the catch-22 of this situation: women are considered second, meaning that they are not even thought of when research is organized that then contributes more to women being considered second. It’s also expensive to create more comprehensive (or multiple) research studies needed to capture all possible consumers connected to the data. This cyclical pattern maintains women’s status as invisible or irrelevant, and keeps them subsumed in male constructs that have dominated, controlled, or usurped spheres of life and function.

Related: 4 mistakes we all make to perpetuate gender bias

What can we do?

Solving these problems starts with demanding better – regardless of our role or gender. we can Influence the decisions that affect this data distortion through actions and decisions.

As members of society, we can become critical analyzers of information, including questioning and/or investigating how data is collected. Ask questions about who is included in a sample population to learn if the people the data will be applied to were adequately represented. If not, question the research and challenge the companies by not investing your money in their products.

As leaders, you should scrutinize your teams—especially those who create research, collect data, or use data. Are they diverse, not only in their technical and professional skills, but also in their personality? Do they represent the population to which the data will be applied? Do they need more diverse and representative perspectives? As a leader, you can also create professional cultures that encourage individuals to challenge protocol and data. Harmony in group and team discussions is not always ideal, as it does not allow for new perspectives and does not encourage the sharing of concerns.

We women can also bring about changes with our money. It’s estimated that women make 70% to 80% of all consumer purchases, yet many companies continue to use data targeted at men – to market to men or highlight their preferences. Women can refuse to put their money into companies that do not include gender equality in research protocols; otherwise they have little motivation to change.

Related: That’s why we still need networking groups for women

Ignoring gender data bias contributes to this and facilitates the placing of women as secondary in our world. Progress as a society and as women begins with acknowledging how representative research and data can create a level playing field. When we realize this, we affect women in the present and future and in all areas of life.

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