While I’ve spent a bunch of time fact-checking these, I’m very interested in correcting any mistakes I may have inadvertently made. If you catch any mistakes, please let me know what I’m wrong about and what’s actually true.

Facts about the US that contradict commonly believed narratives:

1) Regarding political violence, the majority of Americans see it as…a big problem in society and as being “never justified” (liberals and conservatives agree on this), and the substantial majority view it as “always or usually unacceptable” to be happy about a public figure’s death.

2) The majority of murderers have…prior criminal history (e.g., arrests or convictions), and the substantial majority of homicides are committed by men under 45.

3) More than half of murder victims who were not murdered by a family member…also have prior criminal histories (though, of course, this doesn’t mean that they deserve to be murdered).

4) The majority of homicides are committed due to…personal arguments or are related to drug or gang activity, rather than random acts of violence.

5) School shootings kill…vastly fewer children annually than prosaic dangers like unsafe driving (though it’s a horrifying tragedy each time school shootings occur).

6) Mass murders (where 3 or more people are murdered at the same event) are most often…familicide, where a person kills their family, usually committing suicide afterward.

7) Regarding violence, since the 1990s, America has gotten…far less violent (while there was an uptick during the pandemic around 2020, it is still well below the 1990s peak).

8) Compared to alcohol, homicide leads to the death of…very few people (though it’s terrible whenever homicide occurs).

9) The majority of gun-related deaths are…suicides, not homicides.

10) In rural areas, the suicide rate (per million people) is…highest (urban areas actually have lower rates).

11) The vast majority of reported disappearances of children are…relatives taking a child (e.g., custody disputes) or runaways (rather than kidnappings).

12) Most rapes are carried out by…someone the victim already knows (though in about 1 in 5 cases, the perpetrator is a stranger).

13) Women experiencing sexual assault are not…at all uncommon (more than 20% of adult women have been sexually assaulted at some point in their lives).

14) The most dangerous activity that is very common for people under 30 to engage in on a daily basis is…driving in cars.

15) Commercial airline crashes are…incredibly rare (despite the media attention), and commercial flights are far safer than driving per mile (whereas per hour they are closer to being on par).

16) For adults 25 to 35, the biggest killer is…accidental poisoning (which mostly consists of drug overdoses), not car accidents, and considering the whole adult population, opioid related deaths exceed deaths from motor vehicles.

17) Most personal bankruptcy is related to…sudden job loss or illness (which can simultaneously lead to large medical bills and loss of work).

18) The significant majority of federal taxes that the government collects come from…the top 20% of earners.

19) The percent of Americans who pay no federal income tax is…about 35% (though they still pay payroll taxes and sales taxes, and may pay property taxes and state taxes).

20) Regarding health insurance, the vast majority of Americans…are insured (about 90%), and while some people get extremely screwed by the system by being stuck with huge bills they can’t afford due to unavoidable medical challenges, most Americans say they are satisfied with their health insurance, even though they usually also say that the system overall is substantially flawed and needs significant reforms.

21) Most US federal government spending goes to…social security, health care (e.g., Medicaid/Medicare), military-related expenses (e.g., staff costs, veterans, vehicles), and interest payments on national debt (since interest rates have risen).

22) On average, legal immigrants commit crimes…at a lower rate than natural-born citizens.

23) Where immigration status is reliably recorded, undocumented immigrants have an incarceration rate…lower than that of U.S.-born residents.

24) It’s extremely rare that trans people…get murdered (of course, it’s a horrible tragedy when it does occur, and there are uncertainties around data collection); but current data indicates that suicide is a vastly more common life-threatening risk to trans people, and also, that trans people experience a substantially elevated risk of non-fatal violence compared to cis people.

25) Unarmed Black people who are stopped or engaged by the police have…an extremely low chance of being killed by those police (of course, it’s a horrendous tragedy when it does occur); however, Black people are substantially more likely than white people to be stopped by police without clear cause, and are far more likely than white people to be murdered by criminals.

26) Black Americans mostly want the level of police presence in their area…to stay unchanged (i.e., neither be decreased nor increased), with only about 1 in 5 wanting less policing, though most Black Americans do want other major changes to policing to be made.

27) Currently, much of the recycling that occurs…ends up being wasteful once you factor in all extra fuel burned in order to recycle those materials, the amount of “recycled material” that fails to actually be recycled, and alternative enviromental efforts goverment money spent on recyclying could have gone to instead; whether recycling is effective depends on the region as well as the type of material being recyled (e.g., aluminum is especially useful to recycle, whereas plastic recycling tends to be inefficient).

28) Our landfills are…mostly not close to running out of capacity (and when there are shortages, they are almost always local issues).

29) From a danger perspective, nuclear power is…extremely safe (especially when compared to many other sources of power, like coal), as well as very environmentally friendly (with almost no emissions and reliable solutions for storing the toxic waste produced); new reactor designs are dramatically safer than past ones, yet, nuclear power largely is stopped from being cost-effective due to excessive regulations that are extremely costly to comply with.

30) Almost all suffering that humans cause to domesticated land animals is due to…practices at large farms, such as tiny cages that animals spend almost their whole lives in, or being densely packed together in unpleasant conditions with little to no outdoor access and limited ability to engage in their natural behaviors.

31) Most individuals who experience homelessness are homeless for…less than 12 months, but most of the people you see living on city streets, who are typically the most visible homeless people, are experiencing longer-term homelessness.

32) The majority of people who experience chronic homelessness are either…experiencing a drug addiction or a significant mental health challenge, or both (though for some of these people, the addiction or mental health challenge occurred after homelessness began); a non-negligible percent (perhaps 20%, but estimates differ substantially) have neither challenge.

33) The primary causes of high housing prices are…factors that increase the costs of building new housing or that completely prevent it from being built (such as zoning, excessive regulations, lengthy approval processes, and local opposition), as well as, for popular places like New York City, net migration into those areas.

34) The majority of people in prison in the US at any given moment are there for…violent crimes, not non-violent drug-related crimes or victimless offenses – while the substantial majority of convictions are for non-violent crimes (since most crime is non-violent), violent crime typically carries much longer sentences.

35) Almost nobody who is charged with a crime goes to…trial (they mostly take plea bargains).

36) The significant majority of people who are charged with a serious crime and go to trial are…convicted.

37) Regarding the US federal minimum wage, very…few people actually get paid that amount (in part due to higher minimum wages that many states have, and in part due to naturally occurring labor market prices that are simply higher than the federal minimum).

This piece was first written on November 2, 2025, and first appeared on my website on November 17, 2025.

A significant and pretty common problem I see when reading papers in social science (and psychology in particular) is that they present a fancy analysis but don’t show the results of what we have named the “Simplest Valid Analysis” – which is the simplest possible way of analyzing the data that is still a valid test of the hypothesis in question.

This creates two potentially serious problems that make me less confident in the reported results:

1. Fancy analyses impress people (including reviewers), but they are often harder to interpret than simple analyses. And it’s much less likely the reader really understands the fancy analysis, including its limitations, assumptions, and gotchas. So, the fancy analysis can easily be misinterpreted, and is sometimes even invalid for subtle reasons that reviewers, readers (and perhaps the researchers themselves) don’t realize. As a mathematician, I am deeply unimpressed when someone shows me a complex mathematical method when a simple one would have sufficed, but a lot of people fear or are impressed by fancy math, so complex analyses can be a shield that people hide behind.
2. Fancy analyses typically have more “researcher degrees of freedom.” This means that there is more wiggle room for researchers to choose an analysis that makes the results look the way the researcher would prefer they turn out. These choices can be all too easy to justify for many reasons including confirmation bias, wishful thinking, and a “publish or perish” mentality. In contrast, the Simplest Valid Analysis is often very constrained, with few (if any) choices left to the researcher. This makes it less prone to both unconscious and conscious biases.

When a paper doesn’t include the Simplest Valid Analysis, I think it is wise to downgrade your trust in the result at least a little bit. It doesn’t mean the results are wrong, but it does mean that they are harder to interpret.

I also think it’s fine and even good for researchers to include more sophisticated (valid) analyses and to explain why they believe those are better than the Simplest Valid Analysis, as long as the Simplest Valid Analysis is also included. Fancy methods sometimes are indeed better than simpler ones, but that’s not a good reason to exclude the simpler analysis.

Here are some real-world examples where I’ve seen a fancier analysis used while failing to report the Simplest Valid Analysis:

• Running a linear regression with lots of control variables when there is no need to control for all of these variables (or no need to control for more than one or two of the variables)
• Use of ANOVA with lots of variables when really the hypothesis only requires a simple comparison of two means
• Use of a custom statistical algorithm when a very simple standard algorithm can also test the hypothesis
• Use of fancy machine learning when simple regression algorithms may perform just as well
• Combining lots of tests into one using fancy methods rather than performing each test one at a time in a simple way

The problems that can occur when the results of Simplest Valid Analysis aren’t reported was one of the reasons that we decided to include a Clarity Criterion in our evaluation of studies for Transparent Replications. As part of evaluating a study’s Clarity, if it does not present the results of the Simplest Valid Analysis, we determine what that analysis would be, and pre-register and conduct the Simplest Valid Analysis on both the original data and the new data we collect for the replication. Usually it is fairly easy to determine what the Simplest Valid Analysis would be for a research question, but not always. When there are multiple analyses that could be used as the Simplest Valid Analysis, we select the one that we believe is most likely to be informative, and we select that analysis prior to running analyses on the original data and prior to collecting the replication data.

In my view, while it is very important that a study replicates, replication alone does not guarantee that a study’s results reflect something real in the world. For that to be the case, we also have to be confident that the results obtained are from valid tests of the hypotheses. One way to increase the likelihood of that being the case is to report the results from the Simplest Valid Analysis.

My advice is that, when you’re reading scientific results, look for the Simplest Valid Analysis, and if it’s not there, downgrade your trust in the results at least a little bit. If you’re a researcher, remember to report the Simplest Valid Analysis to help your work be trusted and to help avoid mistakes (I aspire always to do so, though there have likely been times I have forgotten to). And if you’re a peer reviewer or journal editor, ask authors to report the Simplest Valid Analysis in their papers in order to reduce the risk that the results have been misinterpreted.

This confuses the public and undermines trust in science and scientists.

Some interesting examples:

1) As COVID-19 spread early in the pandemic, epidemiologists confidently stated many true things about it that were scientifically measured (e.g., rate of spread). Some of them were also equally confidently stating things that were just speculation (e.g., its origin being natural).

2) String theorists told the public many true and interesting things about string theory (e.g., why they feel it’s exciting). Some also confidently claimed very uncertain stuff like:”Superstring theory successfully merges general relativity and quantum mechanics.”

Being charitable, perhaps this could be interpreted not as a claim about superstring theory providing a correct theory of physics but rather as a statement about what superstring theory is doing mathematically. Even if so, though, this is – at the very least – going to be very confusing to those who read it. The statement also makes superstring theory seem like it can claim great achievements that perhaps it can’t.

3) Biologists confidently tell the public many true things about how cells form, how evolution works, and so on. Some, unfortunately, have made overconfident claims about a subject that is extremely uncertain: how life formed on Earth. We have only highly speculative theories.

Let me be clear: most scientists don’t engage in what I’m describing above. But when people claim something has been scientifically PROVEN when it actually hasn’t, this tends to reduce trust in the scientific enterprise and causes people to doubt scientists.

My field (psychology) is squishy enough that (unlike physics/biology) little has truly been PROVEN beyond a doubt. At best, we can usually say that studies have found a relationship or that (based on our own interpretation of the evidence) we believe a certain thing.

This piece was first written on August 13 and first appeared on this site on August 23, 2023.

2) Set units: use the “format” feature to make percentages into actual percentages (ending in %), to make dollar figures into actual dollar figures (starting with $), and so on. This makes it easier to interpret figures at a glance.

3) Use formulas: anything that can be calculated using the “formulas” feature should be (don’t do calculations by hand). Using formulas means numbers are automatically updated if anything changes, reduces human error, and makes it possible to check how a calculation is done.

4) Distinguish inputs and outputs: use a different color for numbers that are automatically-calculated outputs than for those that are inputs (e.g., entered by hand). This makes it easy to see what can be varied (and what is calculated and should not be manually edited).

5) Bold totals and key figures: make totals and key figures stand out by using bold (or color coding) to draw the eye to the most important parts of the sheet.

6) Don’t repeat yourself: If you’re repeating a header more than once in a way that means the same thing each time (e.g., “Total Revenue” appears once in every column), or you find that have another form of repetition, you should restructure your tables to remove the repetition.

7) Use clear names: every column and row should have a name that makes it crystal clear what it actually means. Ambiguous names can lead to confusion and mistakes, especially if others have to read your spreadsheets or if you may return to a spreadsheet you made a long time ago.

8) Center-align column headers and numbers: spreadsheets look neater and are a bit easier to read when the column headers and numbers are aligned in the center of columns rather than using the default (left or right) alignment.

9) Order columns and rows based on importance: put the most important stuff first (to the left and to the top) so that it’s easier to see what’s important immediately.

10) Bonus tips:

• Freeze the header row so that sorting works better and so that it’s clearer what the header is. 
• For columns (or rows) where the bigger numbers are more important than smaller ones, use conditional formatting (with a 3-color scale for numbers that can be both positive and negative or a 2-color scale for ones that are all positive) to make the important numbers pop out.
• Give spreadsheets really clear names so that they are easy to search for (and so that anyone who sees them can quickly understand what each spreadsheet contains)

This piece was first written on May 5, 2023, and first appeared on this site on May 7, 2023.

You have probably heard the phrase “replication crisis.” It refers to the grim fact that, in a number of fields of science, when researchers attempt to replicate previously published studies, they fairly often don’t get the same results. The magnitude of the problem depends on the field, but in psychology, it seems that something like 40% of studies in top journals don’t replicate. We’ve been tackling this crisis with our new Transparent Replications project, and this post explains one of our key ideas.

Replication failures are sometimes simply due to bad luck, but more often, they are caused by p-hacking – the use of fishy statistical techniques that lead to statistically significant (but misleading or erroneous) results. As big a problem as p-hacking is, there is another substantial problem in science that gets talked about much less. Although certain subtypes of this problem have been named previously, to my knowledge, the problem itself has no name, so I’m giving it one: “Importance Hacking.”

Academics want to publish in the top journals in their field. To understand Importance Hacking, let’s consider a (slightly oversimplified) list of the three most commonly-discussed ways to get a paper published in top psychology journals:

1. Conduct valuable research – make a genuinely interesting or important discovery, or add something valuable to the state of scientific knowledge. This is, of course, what just about everyone wants to do, but it’s very, very hard!
2. Commit fraud – for instance, by making up your data. Thankfully, very few people are willing to do this because it’s so unethical. So this is by far the least used approach.
3. p-hack – use fishy statistics, HARKing (i.e., hypothesizing after the results are known), selective reporting, using hidden researcher degrees of freedom, etc., in order to get a p<0.05 result that is actually just a false positive. This is a major problem and the focus of the replication crisis. Of course, false positives can also come about without fault, due to bad luck.

But here is a fourth way to get a paper published in a top journal: Importance Hacking.

4. Importance Hack – get a result that is actually not interesting, not important, and not valuable, but write about it in such a way that reviewers are convinced it is interesting, important, and/or valuable, so that it gets published.

For research to be valuable to society (and, in an ideal world, publishable in top journals), it must be true AND interesting (or important, useful, etc.). Researchers sometimes p-hack their results to skirt around the “true” criterion (by generating interesting false positives). On the other hand, Importance Hacking is a method for skirting the “interesting” criterion.

Importance Hacking is related to concepts like hype and overselling, though hype and overselling are far more general. Importance Hacking refers specifically to a phenomenon whereby research with little to no value gets published in top journals due to the use of strategies that lead reviewers to misinterpret the work. On the other hand, hype and overselling are used in many ways in many stages of research (including to make valuable research appear even more valuable).

One way to understand importance hacking is by comparing it to p-hacking. P-hacking refers to a set of bad research practices that enable researchers to publish non-existent effects. In other words, p-hacking misleads paper reviewers into thinking that non-existent effects are real. Importance Hacking, on the other hand, encompasses a different set of bad research practices: those that lead paper reviewers to believe that real (i.e., existent) results that have little to no value actually have substantial value.

This diagram illustrates how I think Importance Hacking interferes with the pipeline of producing valuable research:

There are a number of subtypes of Importance Hacking based on the method used to make a result appear interesting/important/valuable when it’s not. Here is how I subdivide them:

Types of Importance Hacking

1. Hacking Conclusions: make it seem like you showed some interesting thing X but actually show something else (X′) which sounds similar to X but is much less interesting/important. In these cases, researchers do not truly find what they imply they have found. This phenomenon is also closely connected with validity issues.

• Example 1: showing X is true in a simple video game but claiming that X is true in real life.
• Example 2: showing A and B are correlated and claiming that A causes B (when really A and B are probably both caused by some third factor C, which makes the finding much less interesting).
• Example 3: if a researcher claims to be measuring “aggression,” and couches all conclusions in these terms but is actually measuring milliliters of hot sauce that a person puts in someone else’s food. Their result about aggression will be valid only insofar as it is true that this is a valid measure of aggression.
• Example 4: some types of hacking conclusions would fall under the terms “overclaiming” or “overgeneralizing;” Tal Yarkoni has a relevant paper called The Generalizability Crisis.

2. Hacking Novelty: refer to something in a way that makes it seem more novel or unintuitive than it is. Perhaps the result is already well known or is merely what just about everyone’s common sense would already tell them is true. In these cases, researchers really do find what they claim to have found, but what they found is not novel (despite them making it seem so). Hacking Novelty is also connected to the “Jingle-jangle” fallacy – where people can be led to believe two identical concepts are different because they have different names (or, more subtly, because they are operationalized somewhat differently).

• Example 1: showing something that is already well-known but giving it a new name that leads people to think it is something new. The concept of “grit” has received this criticism; some people claim it could turn out to be just another word for conscientiousness (or already known facets of conscientiousness) – though this question does not yet seem to be settled (different sides of this debate can be found in these papers: 1, 2, 3 and 4).
• Example 2: showing that A and B are correlated, which seems surprising given how the constructs are named, but if you were to dig into how A and B were measured, it would be obvious they would be correlated.
• Example 3: showing a common-sense result that almost everyone already would predict but making it seem like it’s not obvious (e.g., by giving it a fancy scientific name).

3. Hacking Usefulness: make a result seem useful or relevant to some important outcome when in fact, it’s useless and irrelevant. In these cases, researchers find what they claim to have found, but what they find is not useful (despite them making it sound useful).

• Example: focusing on statistical significance when the effect size is so small that the result is useless. Clinicians often distinguish between “statistical significance” and “clinical significance” to highlight the pitfalls of ignoring effect sizes when considering the importance of a finding.

4. Hacking Beauty: make a result seem clean and beautiful when in fact, it’s messy or hard to interpret. In these cases, researchers focus on certain details or results and tell a story around those, but they could have focused on other details or results that would have made the story less pretty, less clear-cut, or harder to make sense of. This is related to Giner-Sorolla’s 2012 paper Science or art: How aesthetic standards grease the way through the publication bottleneck but undermine science. Hacking beauty sometimes reduces to selective reporting of some kind (i.e., selective reporting of measures, analyses, or studies) or at least of selective focus on certain findings and not others. This becomes more difficult with pre-registration; if you have to report the results of planned analyses, there’s less room to make them look pretty (you could just say they’re pretty, but that seems like overclaiming)

• Example: emphasizing the parts of the result that tell a clean story while not including (or burying somewhere in the paper) the parts that contradict that story

Science faces multiple challenges. Over the past decade, the replication crisis and subsequent open science movement have greatly increased awareness of p-hacking as a problem. Measures have begun to be put in place to reduce p-hacking. Importance Hacking is another substantial problem, but it has received far less attention.

If a pipe is leaking from two holes and its pressure is kept fixed, then repairing one hole will result in the other one leaking faster. Similarly, as best practices increasingly become commonplace as a means to reduce p-hacking, so long as the career pressures to publish in top journals don’t let up, the occurrence of Importance Hacking may increase.

It’s time to start the conversation about how Importance Hacking can be addressed.

If you’re interested in learning more about Importance Hacking, you can listen to psychology professor Alexa Tullett and me discussing it on the Clearer Thinking podcast (there, I refer to it as “Importance Laundering,” but I now think “Importance Hacking” is a better name) or me talking about it on the Two Psychologists Four Beers podcast. We also discuss my new project, Transparent Replications, which conducts rapid replications of recently published psychology papers in top journals in an effort to shift incentives and create more reliable, replicable research. If you enjoyed this article, you may be interested in checking our replication reports and learning more about the project.

Did you like this article? If so, you may like to explore the ClearerThinking Podcast, where I have fun, in-depth conversations with brilliant people about ideas that matter. Click here to see a full list of episodes.

Can a chosen definition be “wrong”? No. If you choose a definition, then you can define a sound or series of characters to mean whatever you want them to mean. For instance, if you wanted, you could declare that whenever you say “phloop,” you mean one of those little paper umbrellas that are sometimes found in Piña coladas. That would be weird, but it wouldn’t be “wrong.” But we suggest that there are at least seven ways a definition can be “lousy.” 

By understanding what makes a good definition and what makes a lousy one, you can better formulate your ideas, and you can better spot mistakes in other people’s arguments. For instance, you might be in a situation where you’re trying to define the essence of an idea you came up with or characterize the unique career role that you’ve designed for yourself. Alternatively, you might be struggling to understand a definition that someone else is using, and you want to diagnose why exactly you’re finding it confusing. The words we use are crucial to the success of the interactions we have, and it is very handy to be able to pinpoint when a particular word is making a conversation more confusing than it needs to be. So, here are the things that we think make for lousy definitions! 

1. Miscommunication

If you decide the word “dog” refers to cats, people are going to be very confused.

Similarly, if you’re talking to someone who uses the word “racism” to mean X (say, “an explicitly held and endorsed belief that some racial groups are inferior to others”), and you use it to mean Y (say, “any form of negative generalization or attitude to a racial group, whether it’s implicit or explicit”), your conversation is probably not going to go as well as you would like until you identify that difference in usage. For these reasons, good definitions shouldn’t reuse terms that people are already familiar with or have multiple meanings associated with them. A good way to avoid the latter is to clarify upfront what you mean when you’re using a particular definition if the other person might not know what you mean when using that word.

2. Irrelevance

If you define a “dooooog” to be a dog with more than five legs, you’re not going to find it to be useful for much of anything. Dogs like that probably do exist, but they are not something almost anyone ever needs to refer to. We want our definitions to aim toward the things we are likely to want to reference.

For instance, someone bothered to define the word “Rasceta” to mean the crease commonly found going across a person’s wrist. Presumably, there is some subculture where that is a useful word, but very few people will ever need to know that definition. 

3. Unnaturalness

If you define “dogephant” to include all dogs smaller than 10 pounds AND all elephants more than 8000 pounds, you have not “carved reality at the joints.” Because of mixing things that aren’t clearly alike, using this definition makes communicating more muddled than it needs to be.

Another instance of this phenomenon is our use of the word “selfish.” Sometimes people define the word “selfish” in such a way that it includes both “stealing money from someone” and “sacrificing your own life to save the life of ten others because you feel such a strong emotion of compassion for those people;” it’s about doing things that make you feel “good.” An alternate reading of “selfish” might be much more negative: it’s about taking actions which benefit you at the cost of other peoples’ wellbeing.

4. Opinionatedness

If you define “dogmor” to be “those dog-loving morons who somehow are convinced that dogs are better than cats,” then the definition imports both a debatable opinion and an emotional slant into its meaning, causing usage of this word to be infected with either or both of these things.

For example, the word “sissy” not only suggests that someone — usually a boy or man — embodies feminine qualities but carries with it a negative, insulting connotation. If your aim is to make certain people feel bad, then this might be a good strategy to take, though you might be being a jerk, and that approach doesn’t make for clear, unbiased communication.

5. Ambiguity

If by “dogdog” you mean anything that a dog can like, then your word is (1) hard to use and (2) hard to think about; dogs like a large range of things, and individual dogs also have distinct preferences!

The word “problematic” (when used without clarification) is another (problematic) example of a definition: the problem being referred to could be of many different types and could range from quite objective to just the idiosyncratic, subjective opinion of the writer.

6. Inefficiency 

If you define “doglegs” to be anything with the legs of a dog, and “dogface” to be anything with the face of a dog, etc., then you can talk about walking your dog by saying, “I just got back from walking a creature with doglegs, dogface, dogfur, dogheart, …” But this is a ridiculously inefficient way to talk about your dog! Some definitions make communication substantially more efficient since they compress lots of information you commonly want to express into a small package.

Consider a different instance of this: it is possible to talk about calculus without having a word that means “the derivative” (e.g., by always referring to “limits of functions”), but this is going to be a painful and inefficient way to think and communicate. The word derivative makes ideas in calculus much easier to talk about. 

7. Lack of precision

If you define “floofster” to be any animal with fur, then you will not be communicating very precisely when you say, “I pet my floofster this morning.” You may be saying something true (and rather adorable), but someone will not know if you were petting a dog, a cat, or something more exotic like a lizard wearing a fur coat! Ideally, we want our definitions to focus on just those items or concepts we are trying to communicate.

Similarly, if you say to your friend, “I’m feeling bad,” the ambiguity of the word “bad” makes it harder for them to understand what you’re going through. If you say, “I have a headache,” then it will be easier for your friend to help you. Even better, if true, would be to say, “I have a migraine.” 

So, no chosen definition can be “wrong,” but plenty of definitions are “lousy.” To prevent lousy definitions, you should choose definitions that:

(1) allow clear communication, 

(2) refer specifically to the things of interest, 

(3) carve reality at the joints, 

(4) don’t sneak in debatable opinions/slants, 

(5) are relatively unambiguous, 

(6) express more information in fewer words, and 

(7) allow us to be more exact and specific with our words. 

We hope you found this helpful!

This piece first appeared on the Clearer Thinking blog on March 2, 2021, and was published on this site on December 16, 2022.

Examples of common “ambiguous questions” include debates like:

• Is healthcare a right?

Well, what is a ‘right,’ and is that truly the question here that we mean to be asking?

• Is milk healthy for kids?

Well, what does ‘healthy’ mean, and when we say ‘healthy,’ what alternative are we comparing it to implicitly?

• Is immigration good? 

Well, what is ‘good’? Do we mean what advantages and disadvantages are there to immigration?

• Is obesity a disease?

Well, what is a ‘disease,’ and what are we hoping to determine by asking this question?]

• Is abortion murder?

Well, what are we truly trying to get at by asking if it’s ‘murder’?

• Is marriage defined as being between a man and a woman?

What do we mean by ‘defined’ here, and how will determining the ‘definition’ answer any questions for us?

• Is a trans person who identifies as a woman actually a woman?

What does ‘actually’ mean here, and in what ways does that bear on the reasons we’re having this discussion?

• Should performance-enhancing drugs be banned from sports?

What is a ‘performance-enhancing drug,’ and for what reasons do we want to ban them?

• To focus more on one specific example, consider the question:

“Is it healthier to have a diet that has no meat or that includes meat?”

Different disambiguations of these questions could begin to be tackled empirically, but the empirical methods would differ, and the difficulty level could range from relatively easy to answer to nearly impossible, depending on what we mean by the question. For instance, the questions could be disambiguated as:

• “If we take meat eaters in a society and make them go vegetarian (i.e., meatless), would that make them healthier?” Of course, this is still ambiguous because it will depend on what vegetarian food they are made to eat, and what society we started with, and how we define health (e.g., longevity? absence of chronic disease?). Even if we settle these subquestions though, the causal question is quite challenging to answer and very expensive to study, but could, in principle, be answered with randomized controlled trials (i.e., randomize some people to change to one vegetarian diet, some to change to another vegetarian diet, etc.).

• “On average, when a person decides to switch from a meat-eating diet to a vegetarian (i.e., meatless) one, does it make them healthier?” This could, in principle, be answered (once we decide on a metric for health) with a longitudinal study tracking people across time, but we’d still have to rule out effects of other changes besides the dietary ones that tend to coincide with the dietary changes, which could be tricky.

• “If we were constructing the ‘optimally healthy on average’ human diet, would it include any meat or be all vegetarian?” Well, even if we decide on how to define “healthy,” nutrition scientists probably just don’t know enough about humans to answer this question. Plus, there are SO many possible diets; there is no way we could test them all. But in principle, we could start testing many combinations of foods in carefully controlled experiments where we make study participants switch to a prescribed diet to find combinations that seem to work really well for health. This would at least make a small amount of progress. It could, of course, even turn out that the “healthiest” diet is strongly specific to the individual’s body or that the “healthiest” diet will one day involve foods specifically manufactured for each person’s body.

• “If we look at vegetarians within non-vegetarian cultures, are they healthier than the non-vegetarians around them?” Well, this could depend on which cultures we’re talking about but could be answered with individual-level survey data once we pick a culture and a metric for health.

• “If we look at traditionally vegetarian cultures, are they healthier on average than non-vegetarian cultures?” Well, what do we mean by health? If we settle on a definition of health (e.g., longevity or rate of heart attacks), we could then answer this empirically with group-level statistics about different regions (e.g., what’s the average longevity in the mostly vegetarian vs. mostly non-vegetarian regions). This is probably not what most people mean by the question, but it’s relatively easier to answer than most of the other disambiguations.

Our society cannot reach conclusions effectively with out getting a clear view of our issues.