Numbers feel clean. A single figure—an average, a mean, a typical value—promises to cut through the noise and give us something solid to hold onto. But that promise is often a trap. When we average data that was never meant to be averaged, we don’t simplify reality; we replace it with a tidy fiction. Jerome Leland has spent years watching how information gets packaged for public consumption, and he’s noticed a pattern: the most dangerous mistakes aren’t the obvious falsehoods. They’re the ones that look perfectly reasonable on a spreadsheet.
To see why averaging can go so wrong, you have to understand what an average actually assumes. The arithmetic mean—add everything up, divide by the count—only makes sense if the items you’re adding are fundamentally alike, members of a single coherent category. When they’re not, the average becomes a number that describes nothing real. It’s a statistical ghost, and we keep mistaking it for a living thing.
The Flaw of the “Average Person”
Back in 1945, the Cleveland Health Museum held a contest to find a woman whose body measurements matched the “average American woman” as defined by a recent anthropometric study. They found a theater cashier named Martha Skidmore and crowned her the statistical ideal. The only problem: nobody had checked whether a real person could actually match all those average dimensions at once. When the U.S. Air Force later tried designing cockpits around the average pilot’s measurements, they discovered that out of thousands of pilots, not a single one fit the average across even a handful of key dimensions. The average was a composite that described no actual human being. This is the first way averaging fails: the center point of a multivariate distribution may not correspond to any real instance in the population.
We still make this mistake today, especially in media narratives. The “average household” or the “average voter” is invoked as if such a creature exists. But households vary wildly in size, composition, and income. The average voter is a statistical ghost, smoothing over deep fractures in priorities and values. When a news story says “the average American thinks X,” it’s often papering over the very divisions that are the real story.
Mixing Categories That Should Stay Separate
A second, sneakier problem shows up when we average across categories that are fundamentally different. Picture a county reporting its “average emergency response time.” The county has a dense urban core and a sprawling rural hinterland. In the city, response times clock around 6 minutes. Out in the countryside, it’s more like 25. The combined average might land at 12 minutes. That 12-minute figure is arithmetically correct, but it’s a lie in practice—it describes neither the urban nor the rural experience. It pretends two different systems are one.
You see this same error all over education reporting. A school district announces an average test score that looks respectable, but that single number buries enormous variation between schools, between demographic groups, between programs. The average becomes a tool for hiding inequality rather than exposing it. When journalists repeat these averages without unpacking them, they’re not reporting—they’re helping with the cover-up.
The Ratio Trap
Another classic case of illegitimate averaging involves ratios and rates. Imagine two hospitals. The small one performs 10 procedures and loses 1 patient—a 10% mortality rate. The large one performs 100 procedures and loses 8—an 8% rate. If you naively average the two rates, you get 9%. But the combined mortality across both hospitals is 9 deaths out of 110 procedures, or about 8.2%. The simple average of rates gives equal weight to each hospital, ignoring how many patients they actually treated. This is Simpson’s paradox in action, and it can flip the apparent truth on its head.
In public health reporting, this mistake has real consequences. Averaging infection rates across regions with vastly different population sizes can make an outbreak look milder than it is—or more severe. The right approach weights the rates by the underlying populations, but even then, the aggregate number can hide dangerous local hotspots that need attention now.
The Time-Series Deception
Averaging over time introduces its own brand of distortion. Stock market commentators love to cite average annual returns: “The market has returned an average of 7% per year over the last century.” That smooth 7% line erases crashes, booms, and long grinding periods of stagnation. An investor who entered the market at the wrong moment lived through something nothing like that smooth average. The average hides the sequence of returns, and the sequence is the actual experience of any real participant.
This temporal averaging is especially nasty in climate and weather reporting. A global average temperature rise of 1.5°C sounds almost gentle—until you realize it masks regional extremes where the rise is 3°C or 4°C, with devastating local effects. The average numbs us to the variance, and the variance is where the damage lives.
The Ecological Fallacy in News Narratives
When journalists report on demographic trends, they often stumble into the ecological fallacy: inferring individual behavior from group averages. A classic example is stating that counties with higher average incomes voted a certain way, and then implying that wealthier individuals voted that way. The average of a group doesn’t describe any specific member of that group, and the correlation at the aggregate level can reverse at the individual level. This error is so baked into election coverage that it’s become a structural flaw in how we understand political behavior.
Consider a report that “neighborhoods with higher average education levels have lower crime rates.” The implication is that more educated people commit fewer crimes. But the data is at the neighborhood level, not the individual level. It could be that neighborhoods with higher education levels also have more policing, better lighting, or younger populations. The average is a property of the group, not a property of the people in it. Drawing conclusions about individuals from group averages is a leap that often lands in falsehood.
When the Distribution Matters More Than the Center
In many cases, the average isn’t just misleading—it’s beside the point. Income data is the textbook example. The mean household income in the United States gets yanked upward by a small number of extremely high earners. The median, the 50th percentile, sits substantially lower. But even the median fails to capture the shape of the distribution: the clustering at the bottom, the long tail at the top, the hollowing out of the middle. Reporting only the mean or median income gives no sense of inequality, precarity, or the lived experience of most households.
Journalists who want to convey economic reality should reach for percentile ranges, Gini coefficients, or simply tell stories of specific households at different points on the curve. The average is a shortcut that short-circuits understanding.
Ordinal Data and the Meaningless Mean
Some data cannot be averaged because the numbers themselves are labels, not quantities. Survey questions that ask respondents to rate something on a scale of 1 to 5 produce ordinal data. The difference between “strongly disagree” (1) and “disagree” (2) is not the same as the difference between “agree” (4) and “strongly agree” (5). Yet it’s standard practice to compute an average rating, say 3.7, and treat it as a meaningful measure of sentiment. A 3.7 average could mean that most people were lukewarm, or that the population was polarized between 1s and 5s. The average erases that distinction.
In product reviews, movie ratings, and opinion polls, the average star rating is everywhere. It’s also frequently useless. A film with an average rating of 3 stars might be a bland, universally mediocre experience, or it might be a divisive masterpiece that half the audience adored and half despised. The latter is far more interesting, but the average hides it.
Practical Guidelines for Critical Consumption
Given how pervasive these errors are, a careful reader or reporter needs a mental checklist when confronted with an average. First, ask: What is the underlying distribution? If the data is bimodal, heavily skewed, or has extreme outliers, the average is likely deceptive. Second, ask: Are the categories being mixed? If the average combines apples and oranges—urban and rural, small and large, young and old—it probably should not exist. Third, ask: Is the average being used to make claims about individuals? If so, the ecological fallacy may be in play.
For reporters, the obligation is greater. Presenting an average without context is a form of misinformation. Whenever possible, show the distribution. Use a histogram, a box plot, or even a simple range. If you must report an average, pair it with a measure of spread—standard deviation, interquartile range, or at minimum the minimum and maximum. Let the audience see the shape of the data, not just a single point that may be a statistical mirage.
When Averages Work
This is not an argument against averages altogether. When data is normally distributed and the categories are homogeneous, the mean is a powerful and legitimate summary. If you measure the heights of a thousand randomly selected adult men from the same population, the average height is meaningful and stable. The problem arises when we lazily apply the same tool to data that violates its assumptions. The average is a precision instrument for a specific type of data, not a universal solvent.
In newsrooms, the pressure to simplify is immense. A single number fits in a headline, a chyron, a tweet. A distribution requires explanation, visualization, and nuance. But the cost of that simplification is often accuracy. The public is left with a number that feels true but is actually a distortion. Over time, these distortions accumulate into a fog of misunderstanding that shapes policy, investment, and personal decisions.
FAQ
Why is the average sometimes called a “summary statistic” if it can be so misleading?
The average is a summary statistic in the sense that it reduces many data points to a single number. This is useful when the data is symmetric and unimodal, because the average then represents a typical value. But when the data is skewed, multimodal, or contains outliers, the average summarizes poorly—it gives a number that is not typical of anything in the dataset. The term “summary” does not guarantee accuracy; it only describes the operation of condensing information.
How can I tell if an average in a news article is trustworthy?
Look for accompanying information about the distribution. A trustworthy report will mention the range, the median, or the presence of outliers. If the article only gives an average without any sense of variation, be skeptical. Also consider the source: is the average being used by an advocate to make a point, or by a disinterested analyst to describe a phenomenon? Advocacy often selects the average that supports its case while ignoring the distribution that would undermine it.
What is the difference between mean and median, and when should I prefer one over the other?
The mean is the arithmetic average: sum divided by count. The median is the middle value when data is sorted. The median is resistant to outliers and skewed distributions; it always represents a value that actually occurs in the dataset (or is halfway between two actual values). For income, home prices, and any data with a long tail, the median is usually more informative than the mean. The mean is appropriate for symmetric distributions without extreme values, such as measurement errors or natural physical traits in a homogeneous group.
Can averaging ever be completely avoided in journalism?
Probably not, and it shouldn’t be. There are legitimate uses. The key is to avoid averaging across inappropriate categories and to always provide context about the distribution. A responsible journalist treats the average as a starting point for exploration, not as the final word. When the average is the only number reported, it often becomes a stopping point for thought rather than a starting point.