In 1945, two sculptures named Norma and Normman were created to represent the average man and woman in the United States. These sculptures, based on measurements from thousands of young people, were displayed at the American Museum of Natural History. That same year, a contest sought a real-life embodiment of Norma. Interestingly, none of the nearly 4,000 women who participated matched the so-called “normal” woman. This highlights a recurring issue: the concept of “normal” often fails to accurately describe most people.
In statistics, “normal” refers to a distribution pattern known as the bell curve. This curve shows how values are spread out, with most clustering around the average, or mean. The shape of the curve can vary, being either tall and narrow or long and flat, but it always centers around this average. Importantly, normal in this context describes a pattern, not a single data point. Many human characteristics, like height, follow this normal distribution, where most individuals are close to the average, with fewer being extremely tall or short.
Outside of statistics, “normal” often refers to an average value, like a single number from the center of the bell curve. This simplification can overlook the diversity captured by the full distribution. Norma and Normman’s proportions were based on such averages, which can be misleading when applied to individuals. For example, the Body Mass Index (BMI) categorizes weight relative to height into “underweight,” “normal weight,” “overweight,” and “obese.” However, BMI doesn’t account for factors like body fat percentage, distribution, physical activity, or blood pressure, making it an imperfect health indicator.
When we define normal based on data from a non-representative sample, we risk choosing from the wrong distribution. Much research in behavioral science relies on samples that are WEIRD—Western, educated, industrialized, rich, and democratic. This can skew norms, even in studies where these factors seem unrelated. For instance, the Müller-Lyer optical illusion is perceived differently by various demographic groups. While American undergraduates often see one line as longer, the San people of the Kalahari do not experience this illusion.
Using limited or inaccurate definitions of normal can have harmful consequences. Historically, the concept of normal has been used to justify exclusion and discrimination. The Eugenics Movement of the early 20th century used it to legitimize violence and even extermination of those considered abnormal. Today, people still face discrimination based on disabilities, mental health, sexual orientation, gender identity, and other traits deemed “not normal.” However, true normalcy lies in the diversity of our bodies, minds, perceptions, and ideas. Embracing this diversity is essential for a more inclusive and understanding world.
Research the creation of the Norma and Normman sculptures and their historical significance. Discuss in a group how these representations reflect societal views of “normal” at the time. Consider how these views have evolved and what factors have influenced changes in the perception of normalcy.
Using a dataset of your choice, create a bell curve to visualize the normal distribution. Identify the mean, median, and mode, and discuss how these measures of central tendency relate to the concept of “normal.” Reflect on how this statistical understanding can be applied to real-world scenarios.
Investigate the Body Mass Index (BMI) and its limitations as a health indicator. Write a short essay critiquing its use in medical and fitness contexts, considering factors it overlooks. Propose alternative methods or additional metrics that could provide a more comprehensive understanding of health.
Conduct an experiment to explore cultural differences in perception, such as the Müller-Lyer illusion. Gather a diverse group of participants and record their responses. Analyze the results to see if there are variations based on cultural backgrounds, and discuss the implications of these findings.
Participate in a workshop focused on embracing diversity and challenging narrow definitions of normal. Engage in activities that highlight the value of diverse perspectives and experiences. Reflect on how you can contribute to a more inclusive environment in your personal and professional life.
In 1945, two sculptures were created to represent the average man and woman in the United States and were exhibited at the American Museum of Natural History. Based on measurements taken from tens of thousands of young individuals, they were named Norma and Normman. That same year, a contest was launched to find a living embodiment of Norma. The term “normal” is often used as a synonym for “typical,” “expected,” or even “correct.” By that logic, most people should fit the description of normal. However, not one of the nearly 4,000 women who participated in the contest matched Norma, the supposedly “normal” woman. This discrepancy is not unique to Norma and Normman; time and again, so-called normal descriptions of our bodies, minds, and perceptions have turned out to match almost no one. Yet, much of the world is constructed around a foundation of normalcy.
So, what does normal actually mean, and should we rely on it so heavily? In statistics, a normal distribution describes a set of values that fall along a bell curve. The average, or mean, of all the values is at the center, with most other values falling within the hump of the bell. These curves can be tall, with most values inside a narrow range, or long and flat, with only a slight bias towards the average. What makes the distribution normal is that it follows this curved shape. Normal doesn’t describe a single data point but rather a pattern of diversity. Many human traits, like height, follow a normal distribution. While some individuals are very tall or very short, most people fall close to the overall average.
Outside of statistics, normal often refers to an average—like the single number pulled from the central part of the bell curve—that eliminates the nuance of the normal distribution. Norma and Normman’s proportions were derived from such averages. When applied to individuals, whether someone is considered normal usually depends on how closely they align with this average. At best, such definitions of normal fail to capture variation. However, our calculations of normal can be even more flawed.
Take the Body Mass Index (BMI), for example. BMI is a measure of weight relative to height, with different ratios categorized as “underweight,” “normal weight,” “overweight,” and “obese.” Generally, only BMIs that correspond to normal weight are considered healthy. However, BMI is not always an accurate predictor of health or what constitutes a healthy weight. It does not take into account body fat percentage, body fat distribution, levels of physical activity, or blood pressure. Yet, those who fall outside the so-called normal range are often advised that losing or gaining weight will improve their health.
When we apply a standard of normal to all of humanity based on data from a non-representative sample, we are not just choosing one point on the distribution; we are selecting it from the wrong distribution. Much behavioral science research draws from samples that are often WEIRD—meaning Western, educated, industrialized, rich, and democratic. These characteristics can skew norms even in research that doesn’t have an obvious link to them.
Consider the famous Müller-Lyer optical illusion: it’s common to perceive one of the two lines as longer, even though they are the same length—at least if you’re an American undergraduate. A team of anthropologists and psychologists found that other demographic groups were much less susceptible to this illusion; for instance, members of the San people of the Kalahari were not susceptible at all. When limited or inaccurate definitions of normal are used to make decisions that impact people’s lives, they can cause real harm.
Historically, such concepts of normal have been hugely influential. The Eugenics Movement of the early 20th century weaponized the concept of normal, using it to justify exclusion, violence, and even extermination of those deemed not normal. To this day, individuals are often targeted and discriminated against based on disabilities, mental health issues, sexual orientations, gender identities, and other features deemed “not normal.” However, the reality is that the differences in our bodies, minds, perceptions, and ideas about the world around us—in short, diversity—is the true normal.
Normal – In statistics, ‘normal’ refers to a type of distribution that is symmetric and bell-shaped, where most of the observations cluster around the central peak and probabilities for values taper off equally in both directions from the center. – The professor explained that many natural phenomena, such as heights and test scores, often follow a normal distribution.
Statistics – Statistics is the science of collecting, analyzing, interpreting, presenting, and organizing data. – In our sociology class, we used statistics to analyze the survey data collected from various communities.
Distribution – In statistics, a distribution describes how the values of a variable are spread or dispersed. – The distribution of income in the region was skewed, indicating a significant disparity between the wealthiest and the poorest residents.
Average – The average is a measure of central tendency that is calculated by summing all the values in a dataset and dividing by the number of values. – The average income of the surveyed households was calculated to understand the economic status of the community.
Diversity – Diversity refers to the presence of differences within a given setting, encompassing various dimensions such as race, ethnicity, gender, age, and more. – The study highlighted the importance of diversity in workplaces for fostering innovation and creativity.
Sample – A sample is a subset of a population selected for measurement, observation, or questioning to provide statistical information about the population. – The researchers used a random sample of 500 individuals to ensure the survey results were representative of the entire population.
Behavior – Behavior refers to the actions or reactions of individuals or groups in response to external or internal stimuli. – The sociologist studied the behavior of individuals in large groups to understand social dynamics.
Norms – Norms are the informal guidelines or rules that govern behavior in a society or group. – The study examined how cultural norms influence individual behavior and societal expectations.
Health – In sociology, health refers to the state of physical, mental, and social well-being, and not merely the absence of disease or infirmity. – The research focused on the social determinants of health and how they affect different communities.
Discrimination – Discrimination is the unjust or prejudicial treatment of different categories of people, especially on the grounds of race, age, or sex. – The paper analyzed the impact of discrimination on employment opportunities and economic outcomes.
