Rubin Vase Reporting

Jesse Singal had an interesting post in his (subscriber only) newsletter this week about a some articles promoting an Amnesty International report that ran under the headline “Amnesty reveals alarming impact of online abuse against women“.  I was intrigued because I love dissections of survey data, and this didn’t disappoint. He noted some inappropriate extrapolations from the results (the Mozilla article claimed that data showed women were harassed more than men online, but the Amnesty survey didn’t survey any men and thus has no comparison), and also that the numbers were a little lower than he thought. Overall in 8 countries, an average of 23% of women had experienced online harassment, with an average of 11% saying they’d experienced online harassment more than once.

This statistic struck me as interesting, because it sounds really different depending on how you phrase it. From the Amnesty article:

Nearly a quarter (23%) of the women surveyed across these eight countries said they had experienced online abuse or harassment at least once, ranging from 16% in Italy to 33% in the US.

If you reverse the language, it reads like this:

“Over three quarters (77%) of the women surveyed across these eight countries said they had never experienced online abuse or harassment even once, ranging from 84% in Italy to 67% in the US.”

Now it is possible those two paragraphs sound exactly the same to you, but to me they give slightly different impressions. By shifting the focus from the positive responses to the negative, two reporters could report the exact same data but give slightly different impressions.

While reading this, all I could think of was the famous Rubin Vase illusion. If you don’t recognize the name, you will almost certainly recognize the picture: 

It struck me as a good analogy for a certain type of statistics reporting, enough so that I decided to give it a name:

Rubin Vase Reporting: The practice of grounding a statistic in either the positive (i.e. % who said yes) or negative (i.e. % who said no) responses in order to influence the way the statistic is read and what it appears to show.

Now of course not every statistic is reported this way intentionally (after all you really do have to pick one way to report most statistics and then stick with it), but it is something to be aware of. Flipping statistics around to see how you feel about them when they’re said in the reverse can be an interesting practice.

Also, I have officially updated my GPD Lexicon page, so if you’re looking for more of these you may want to check that out! I have 19 of these now and have been pondering putting them in to some sort of ebook with illustrations, just for fun. Thoughts on that also welcome.

Reporting the High Water Mark

Another day, another weird practice to add to my GPD Lexicon.

About two weeks ago, a friend sent me that “People over 65 share more fake news on Facebook” study to ask me what I thought. As I was reviewing some of the articles about it, I noticed that they kept saying the sample size was 3,500 participants. As the reporting went on however, the articles clarified that not all of those 3,500 people were Facebook users, and that about half the sample opted out. Given that the whole premise of the study was that the researchers had looked at Facebook sharing behavior by asking people for access to their accounts, it seemed like that initial sample size wasn’t reflective of those used to obtain the main finding. I got curious how much this impacted the overall number, so I decided to go looking.

After doing some follow up with the actual paper, it appears that 2,771 of those people had Facebook to begin with,  1,331 people actually enrolled in the study, and 1,191 were able to link their Facebook account to the software the researchers needed. So basically the sample size the study was actually done on is about a third of the initially reported value.

While this wasn’t necessarily deceptive, it did strike me as a bit odd. The 3,500 number is one of the least relevant numbers in that whole list. It’s useful to know that there might have been some selection bias going on with the folks who opted out, but that’s hard to see if you don’t report the final number.  Other than serving as a selection bias check though (which the authors did do), 63% of the participants had no link sharing data collected on them, and thus are irrelevant to the conclusions reported.  I assumed at first that reporters were getting this number from the authors, but it doesn’t seem like that’s the case.  The number 3,500 isn’t in the abstract. The press release uses the 1,300 number. From what I can tell, the 3,500 number is only mentioned by itself in the first data and methods section, before the results and “Facebook profile data” section clarify how the interesting part of the study was done. That’s where they clarify that 65% of the potential sample wasn’t eligible or opted out.

This was not a limited way of reporting things though, as even the New York Times went with the 3,500 number. Weirdly enough, the Guardian used the number 1,775, which I can’t find anywhere. Anyway, here’s my new definition:

Reporting the high water mark: A newspaper report about a study that uses the sample size of potential subjects the researchers started with, as opposed the sample size for the study they subsequently report on.

I originally went looking for this sample size because I always get curious how many 65+ plus people were included in this study. Interestingly, I couldn’t actually find the raw number in the paper. This strikes me as important because if older people are online in smaller numbers thank younger ones, the overall number of fake stories might be larger among younger people.

I should note that I don’t actually think the study is wrong. When I went looking in the supplementary table, I noted that the authors mentioned that the most commonly shared type of fake news article was actually fake crime articles. At least in my social circle, I have almost always seen those shared by older people rather than younger ones.

Still, I would feel better if the relevant sample size were reported first, rather than the biggest number the researchers looked at throughout the study.

GPD Lexicon: Proxy Preference

It’s been a little while since I added anything to the GPD Lexicon, but I got inspired this week by a Washington Post article on American living situations. It covered a Gallup Poll that asked people an interesting question:

“If you could live anywhere you wished, where would you prefer to live — in a
big city, small city, suburb of a big city, suburb of a small city, town, or rural area?”

The results were then compared to where people actually live, to give the following graph:

Now when I saw this I had a few thoughts:

  1. I wonder if everyone actually knew what the definition of each of those was when they answered.
  2. I wonder if this is really what people want.

The first was driven by my confusion over whether the town I grew up in would be considered a town or a “small city suburb”.

The second thought was driven by my deep suspicion that almost 30% of the US actually wanted to live in rural areas, whereas only half that number actually live in one. While I have no doubt that many people actually do want to live in rural areas, it seems like for at least some people that might be a bit of a proxy for something else. For example, one of the most common reason for moving away from rural areas is to find work elsewhere. Did saying you wanted to live in a rural area represent (for some people) a desire to not have to work or to be able to work less? A desire to not have economic factors influence where you live?

To test this theory, I decided to ask a few early to mid 20s folks at my office where they would live if they could live anywhere. All of them currently live in the city, but all gave different answers.  This matched the Gallup poll findings, where 29% of 18-29 year olds were living in cities, but only 17% said they wanted to. As they put it:

“One of the most interesting contrasts emerges in reference to big-city suburbs. The desire to live in such an area is much higher among 18- to 29-year-olds than the percentage of this age group who actually live there. (As reviewed above, 18- to 29-year-olds are considerably more likely to want to live in a big-city suburb than in a big city per se.)”

Given this, it seems like if I asked any of my young coworkers if they wanted to rent a room from me in my large city suburb home, they’d say yes. And yet I doubt they actually would. When they were answering,  almost none of them were talking about their life as it currently stands, but more what they hope their life could be. They wanted to get married, have kids, live somewhere smaller or in the suburbs. Their vision of living in the suburbs isn’t just the suburbs, it’s owning their own home, maybe having a partner, a good job, and/or kids. They don’t want a room in my house. They want their own house, and a life that meets some version of what they call success.

I think this is a version of what economists call a “revealed preference“, where you can tell what people really want by what they actually buy. In this version though, people are using their answers to one question to express other desires that are not part of the question. In other words this:

Proxy Preference: A preference or answer given on a survey that reflects a larger set of wants or needs not reflected in the question.

An example: Some time ago, I saw a person claiming that women should never plan to return to the workforce after having kids, because all women really wanted to work part time. To prove this, she had pointed to a survey question that asked women “if money were not a concern, what would your ideal work set up be?”. Unsurprisingly, many women said they’d want to work part time. I can’t find it now, but that question always seemed unfair to me. Of course lots of people would drop their hours if they had no money concerns! While many of us are lucky enough to like a lot of what we do, most of us are ultimately working for money.

A second example: I once had a pastor mention in a sermon that as a high schooler he and his classmates had been asked if they would rather be rich, famous, very beautiful or happy. According to his story, he was one of the only people who picked “happy”. When he asked his classmates why they’d picked the other things, they all replied that if they had those things they would be happy. It wasn’t that they didn’t want happiness, it was that they believed that wealth, fame and beauty actually led directly to happiness.

Again, I don’t think everyone who says they want to live in a rural area only means they want financial security or a slower pace of life, but I suspect they might. It would be interesting to narrow the question a bit to see what kind of answers you’d get. Phrasing it “if money were no object, where would you prefer to live today?” might reveal some interesting answers. Maybe ask a follow up question about “where would you want to live in 5 or 10 years?”, which might reveal how much of the answer had something to do with life goals.

In the meantime though, it’s good to remember that when a  large number of people say they’d prefer to do something other than what they are actually doing, thinking about the reasons for the discrepancy can be revealing.

Delusions of Mediocrity

I mentioned recently that I planned on adding monthly(ish) to my GPD Lexicon page, and my IQ post from Sunday reminded me of a term I wanted to add. While many of us are keenly aware of the problem of “delusions of grandeur” (a false sense of one’s own importance), I think fewer people realize that thinking oneself too normal might also be a problem.

In some circles this happens a lot when topics  like IQ or salary come up, and a bunch of college educated people sit around and talk about how it’s not that much of an advantage to have a higher IQ or having an above average salary. While some people saying this are making good points, some are suffering a delusion of mediocrity. They are imagining in these discussions that their salary or IQ is “average” and that everyone is working in the same range as them and their social circle. In other words, they are debating IQ while only thinking about those with IQs above 110 or so, or salaries above the US median of $59,000.  In other words:

Delusions of Mediocrity: A false sense of one’s one averageness. Typically seen in those with above average abilities or resources who believe that most people live like they do.

Now I think most of us have seen this on a personal level, but I think it’s also important to remember it on a research level. When research finds things like “IQ is correlated with better life outcomes”, they’re not just comparing IQs of 120 to IQs of 130 and finding a difference….they’re comparing IQs of 80 to IQs of 120 and finding a difference.

On an even broader note, psychological research has been known to have a WEIRD problem. Most of the studies we see describing “human” behavior are actually done on those in Western, educated, industrialized, rich and democratic countries (aka WEIRD countries) that do NOT represent that majority of the world population. Even things like optical illusions have been found to vary by culture, so how can we draw conclusions about humanity while drawing from a group that represents only 12% of the world’s population? The fact that we don’t often question this is a mass delusion of mediocrity.

I think this all gets tempting because our own social circles tend to move in a narrow range. By virtue of living in a country, most of us end up seeing other people from that country the vast majority of the time. We also self segregate by neighborhood and occupation. Just another thing to keep in mind when you’re reading about differences.

Tidal Statistics

I’m having a little too much fun lately with my “name your own bias/fallacy/data error” thing, so I’ve decided I’m going to make it a monthly-ish feature. I’m gathering the full list up under the “GPD Lexicon” tab.

For this month, I wanted to revisit a phrase I introduced back in October: buoy statistic. At the time I defined the term as:

Buoy statistic: A statistic that is presented on its own as free-floating, while the context and anchoring data is hidden from initial sight.

This was intended to cover a pretty wide variety of scenarios, such as when we hear things like “women are more likely to do thing x” without being told that the “more likely” is 3 percentage points over men.

While I like this term, today I want to narrow it down to a special subcase: tidal statistics. I’m defining those as…..

Tidal Statistic: A metric that is presented as evidence of the rise or fall of one particular group, subject or issue, during a time period when related groups also rose or fell on the same metric

So for example, if someone says “after the CEO said something silly, that company’s went down on Monday” but they don’t mention that the whole stock market went down on Monday, that’s a tidal statistic. The statement by itself could be perfectly true, but the context changes the meaning.

Another example: recently did an article about racial segregation in schools in which they presented this graph:

Now this graph initially caught my eye because they had initially labeled it as being representative of the whole US (they later went back and corrected it to clarify that this was just for the south), and I started to wonder how this was impacted by changing demographic trends. I remembered seeing some headlines a few years back that white students were now a minority-majority among school age children, which means at least some of that drop is likely due a decrease in schools whose student populations are > 50% white.

Turns out my memory was correct, and according to the National Center for Education Statistics, in the fall of 2014, white students became a minority majority in the school system at 49.5% of the school age population.  For context, when the graph starts (1954) the US was about 89% white. I couldn’t find what that number was for just school age kids, but it was likely much higher than 49.5%.   So basically if you drew a similar graph for any other race, including white kids, you would see a drop. When the tide goes down, every related metric goes down with it.

Now to be clear, I am not saying that school segregation isn’t a problem or that the Vox article gets everything wrong. My concern is that graph was used as one of their first images in a very lengthy article, and they don’t mention the context or what that might mean for advocacy efforts. Looking at that graph, we have no idea what percentage of that drop is due to a shrinking white population and what is due to intentional or de facto segregation. It’s almost certainly not possible to substantially raise the number of kids going to schools who have more than 50% white kids, simply because the number of schools like that is shrinking.  Vox has other, better, measures of success further down in the article, but I’m disappointed they chose to lead with one that has a major confounder baked in.

This is of course the major problem with tidal statistics. The implication tends to be “this trend is bad, following our advice can turn it around”. However, if the trend is driven by something much broader than what’s being discussed, any results you get will be skewed. Some people exploit this fact, some step in to it accidentally, but it is an interesting way that you can tell the truth and mislead at the same time.

Stay safe out there.


Magnitude Problems, Now With Names

In my last blog post, I put out a call for name ideas for a particular “potentially motivated failure to recognize that the magnitude of numbers matters” problem I was seeing, and man did you all come through! There were actually 3 suggestions that got me excited enough that I wanted to immediately come up with definitions for them, so I now have 3 (actually 4) new ways to describe my problem. A big thanks to J.D.P Robinson, Korora, and the Assistant Village Idiot for their suggestions.

Here are the new phrases:

Hrair Line: a somewhat arbitrary line past which all numbers seem equally large

Based on the book “Watership Down” where characters use the word “hrair” to mean “any number greater than 4”.  We all have a line like this when numbers get big enough….I doubt any of us truly registers the difference between a quadrillion and a sextillion unless we encounter those numbers in our work. Small children do this with time (anything other than “right now” is “a long time”), and I’d guess all but the richest of us do this with money (a yearly salary of $10 million and $11 million are both just “more than I make” to me). On it’s own, this is not necessarily a bad thing, but rather a human tendency to only wrap our heads around the number values that matter most to us. This tendency can be misused however, which is where we get….

The Receding Hrair Line: The tendency to move one’s hrair line based on the subject under discussion, or for one group and not another, normally to benefit your argument

Also known (in my head) as the Soros/Koch brothers problem. Occasionally you’ll see references to charitable gifts by those controversial figures, and it’s always a little funny to see how people perceive those numbers based on their pre-conceived feelings about Soros/Koch. I’ve seen grants of $5000 called “a small grant” or be credited with helping fund the whole organization. You could certainly defend either stance in many cases, but my concern is that people frequently seem to start from their Soros/Koch feelings and then bring the numbers along for the ride. They are not working from any sort of standard for what a $5000 grant means to a charity, but rather a standard for what a George Soros or Koch brothers gift means and working backwards. This can also lead too….

Mountain-Molehill Myopiathe tendency to get so fixated on an issue that major changes in magnitude of the numbers involved do not change your stance. Alternatively, being so fixated on an issue that you believe that any change to the number completely proves your point.

A close relative of number blindness, but particularly focused on the size of the numbers. Taking my previous Soros/Koch example, let’s say someone had defend the “a $5000 grant is not a big deal” stance. Now let’s say that there was a typo here, and it turned out that was a $50,000  or a $500 grant. For most people, this would cause you to stop and say “ok, given this new information, let me rethink my stance”. For those suffering from Mountain-Molehill Myopia however, this doesn’t happen. They keep going and act like all their previous logic still stands. This is particularly bizarre, given that most people would have no problem with you pausing to reassess given new information. All but the most dishonest arguers are going to hold you accountable for previous logic if new information comes up. The refusal to do so actually makes you more suspect.

The alternative case here is when someone decides that a small change to the numbers now means EVERYTHING has changed. For example, let’s say the $5000 turns out to be $4900 or $5100. That shouldn’t change anything (unless there are tax implications that kick in at some level of course), but sometimes people seriously overreact to this. You said $5000 and it turns out it was $4900, this means your whole argument is flawed and I automatically win.

There is clearly a sliding scale here, as some changes are more borderline. A $5000 grant vs a $2000 grant may be harder to sort through. For rule of thumb purposes, I’d say an order of magnitude change requires a reaction, and less than that is a nuanced change. YMMV.

Now, all of these errors can be annoying in a vacuum, but they get worse when onlookers start jumping in. This is where you get…..

Pyrgopolynices’ numbers: Numbers that are wrong or over-inflated, but that you believe because they are supported by those around you due to tribal affiliations rather than independent verification

Based on the opening scene of  Plautus’  Braggart Soldier, Korora provided me with the context for this one (slightly edited from the original comment):

…the title character’s parasītus , or flatterer-slave, is repeating to his master said master’s supposed achievements on the battlefield:

Artotrogus:. I remember: One hundred fifty in Cilicia. A hundred in Scytholatronia*, thirty Sardians, sixty Macedonians. Those are the men thou slewest in one day.
Pyrgopolynices: How many men is that?
Artotrogus: Seven thousand.
Pyrgopolynices: It must be as much. [Thou] correctly hast the calculation.

*there is no such place

After reading this I got the distinct feeling that we did away with flatterer-slaves, and replaced them with social media.

As someone who likes to correct others numbers, you’d think I’d be all about chiming in on Facebook/Twitter/whatever  conversations about numbers or stats, but I’m not. Starting about 3 years ago, I stopped correcting anyone publicly and started messaging people privately when I had concerns about things they posted. While private messages seemed to get an amiable response and a good discussion almost 90% of the time, correcting someone publicly seemed to drive people out of the woodwork to claim that those numbers were actually right. Rather than acknowledge the error as they would privately, my friends would then turn their stats claims in to Pyrgopolynices’ numbers….numbers that people believed because other people were telling them they were true. Of course those people were only telling them they were true because someone on “their side” had said them to begin with, so the sense of check and balances was entirely fictitious.

Over the long term, this can be a very dangerous issue as it means people can go years believing certain things are true without ever rechecking their math.

That wraps it up! Again, thank you to J.D.P Robinson for mountain-molehill myopia, AVI for throwing the word “hrair” out there, and Korora for the backstory on Pyrgopolynices’ numbers. In related news, I think I may have to start a “lexicon” page to keep track of all of these.

The (Magnitude) Problem With No Name

As most of you know, I am a big fan of amusing myself by coining new names for various biases/numerical tomfoolery I see floating around on the internet. I have one that’s been bugging me for a little while now, but I can’t seem to find a good name for it. I tried it out on a bunch of people around Christmas (I am SUPER fun at parties guys), but while everyone got the phenomena, no one could think of a pithy name. Thus, I turn to the internet.

The problem I’m thinking of is a specific case of what I’ve previously called Number Blindness  or “The phenomena of becoming so consumed by an issue that your cease to see numbers as independent entities and view them only as props whose rightness or wrongness is determined solely by how well they fit your argument”. In this case though, it’s not just that people don’t care if their number is right or wrong, it’s that they seem oddly unmoved by the fact that the number they’re using isn’t even the right order of magnitude. It’s as though they think that any “big” number is essentially equal to any other big number, and therefore accuracy doesn’t matter any more.

For example, a few weeks ago Jenna Fischer (aka Pam from the Office) got herself in trouble by Tweeting out (inaccurately) that under the new tax bill teachers could no longer deduct their classroom expenses. She deleted it, but while I was scrolling through the replies I came across an exchange that went something like this:

Person 1: Well teachers wouldn’t have to buy their own supplies if schools stopped paying their football coaches $5 million a year

Person 2: What high school pays their coach $5 million a year?

Person 3: 28 coaches in Texas make over $120,000 a year.

Person 2: $120,000 is not $5 million.

Person 3: Well that’s part of an overall state budget of $20-25 million just for football coaches. (bs king’s note: I couldn’t find a source for this number, none was given in the Tweet)

Person 2: ….

Poor person 2.

Now clearly there was some number blindness here….person 1 and 3 only seemed to care about the idea that numbers could support their cause, not the accuracy of said numbers. But it was the stunning failure to recognize order of magnitude that took my breath away. How could you seriously reply to a comment about $5 million dollar salaries with an article about $120,000 dollar salaries and feel you’d proved a point? Or respond to a second query with an overall state budget, which is an order of magnitude higher than that? It’s like some sort of big number line got crossed, and now it’s all fair game.

I suspect this happens more often the bigger the numbers get….people probably drive astronomers nuts by equating things like a billion light years and a trillion light years away. Given that I’ve probably done this I won’t get too cocky here, but I would like a name for the phenomenon. Any thoughts are appreciated.

Buoy Statistics

Okay, this is going to be another one of those posts where I make up a term for something I’m seeing that annoys me. You’ve been warned.

When I was a little kid, I remember one of the first times I ever saw a buoy in the ocean. I don’t remember how old I was, but I was probably 5 or so, and I thought the buoy was actually somebody’s ball that had floated away. As the day went on, I remember being amazed that it managed to stay so close to the same spot without moving…it was far from shore (at least to a 5 year old) but somehow it never disappeared entirely. I think my Dad must have noticed me looking at it because he teased me about it for a bit, but he finally told me it was actually anchored with a chain I couldn’t see. Life lessons.

I think about that feeling sometimes when I see statistics quoted in articles with little context. It’s always something like “75% of women do x, which is more than men”, and then everyone makes comments about how great/terrible women are for awhile. 5 paragraphs down you find out that 72% of men also do x, meaning all of the previous statements were true, but are a little less meaningful in context. What initially looked like a rather interesting free floating statistic was actually tied to something bigger. It may not stop being interesting or useful, but it certainly changes the presentation a bit. In other words:

Buoy statistic: A statistic that is presented on its own as free-floating, while the context and anchoring data is hidden from initial sight.

I see buoy statistics most often when it comes to group differences. Gender, racial groups, political groups….any time you see a number with what one group does without the number for the other half, I’d get suspicious.

For example, a few years ago, a story broke that the (frequently trolling) Public Policy Polling Group had found that 30% of Republican voters supported bombing the fictional city of Agrabah from the movie Aladdin. This got some people crowing about how dumb Republicans were, but a closer read showed that 36% of Democrats opposed it. Overall, an almost identical number of each party (43% vs 45%) had an opinion about a fictional city. Now this was a poll question designed to get people to say dumb things, and the associated headlines were pure buoy statistics.

Another example was around a Github study from a few years ago that showed that women had a lower acceptance rate of their pull requests if their user name made it clear they were female (71.8% to 62.5%). Some articles ended up reporting that they got far fewer requests accepted than men, but it turns out that men actually got about 64% of their requests accepted. While it was true the drop off was bigger from gender-neutral names (men went from about 68% to about 64%), 62.5% vs 64% is not actually “far fewer”.  (Note: numbers are approximate because, annoyingly, exact numbers were not released)

I’m sure there are other examples, but basically any time you get impressed by a statistic, only to feel a bit of a let down when you hear the context, you’ve hit a buoy statistic. Now, just like with buoys, these statistics are not without any use. One of the keys to this definition is that they are real statistics, just not always as free-floating as you first perceive them. Frequently they are actually the mark of something legitimately interesting, but you have to know how to take them. Context does not erase usefulness, but it can make it harder to jump to conclusions.


A few weeks ago, I wrote a post about a phenomena I had started seeing that I ended up dubbing premature expostulation. I defined this phenomena as “The act of claiming definitively that a person, group or media outlet has not reported on, responded to or comment on an event or topic, without first establishing whether or not this is true. ” Since writing that post, I have been seeing mention of a related phenomena that I felt was distinct enough to merit its own term. In this version, you actually have checked to see what various sources say, enough that you cite them directly, but you misrepresent what they actually say anyway. More formally, we have:

Misreprecitation: The act of directly citing a piece of work  to support your argument, when even a cursory reading of the original work shows it does not actually support your argument.

Now this does not necessarily have to be done with nefarious motives, but it is hard to think of a scenario in which this isn’t incredibly sketchy. Where premature expostulation is mostly due to knee jerk reactions, vagueness and a failure to do basic fact checking, misreprecitation requires a bit more thought and planning. In some cases it appears to be a pretty direct attempt to mislead, in others it may be due to copying someone else’s interpretation without checking it out yourself, but its never good for your argument.

Need some examples? Let’s go!

The example that actually made me think of this was the recent kerfluffle over Nancy MacLean’s book “Democracy in Chains”. Initially met by praise as a leftist take down of right wing economic thought, the book quickly got embroiled in controversy when (as far as I can tell) actual right wing thinkers started reading it. At that point several of them who were familiar with the source material noted that quotes were chopped up in ways that dramatically changed the meaning, and other contextual problems. You can read a pretty comprehensive list of issues here, and overview of the problems and links to all the various responses here, and Vox’s (none to flattering) take here. None of it makes MacLean look particularly good, most specifically because this was supposed to be a scholarly work. When your citations are your strong point, your citations better be correct.

I’ve also seen this happen quite a bit with books that endorse popular diets. Carbsane put together a list of issues in the citations of the low carb book “Big Fat Surprise”, and others have found issues with vegan promoting books. While some of these seem to be differences in interpretation of evidence, some are a little sketchier. Now, as with premature expostulation, some of these issues don’t change the fundamental point….but some do. Overall a citation avalanche is no good if it turns out you had to tweak the truth to get there.

I think there’s three things that cause a particularly fertile breeding ground for misreprecitation: 1) an audience who is sympathetic to your conclusions and 2) an audience who is unlikely to be familiar with the source documents 3) difficulty accessing source documents. That last point may be why books are particularly prone to this error, since you’d have to actually put the book down and go look up a reference. This also may be a case where blogs have the accuracy advantage due to being so public. I know plenty of people who read blogs they don’t agree with, but I know fewer who would buy a whole book dedicated to discrediting their ideas. That increases the chances that no critical person will read your book, they have less recourse once they do read it (notes in the margin aren’t as good as a comments section), and it’s harder for anyone to fact check. Not saying bloggers can’t do it, just thinking they’d be called on it faster.

Overall it’s a pretty ridiculous little trick, as the entire point of citing others work should be to strengthen your argument. In the best case scenario, people could be confused because they misread/failed to understand/copied an interpenetration of the work they read someone else make. In the worst case scenario, they know what they are doing and are counting on their in-group not actually checking their work. Regardless, it needed a name, and now it has one.

Premature Expostulation

In my last post, I put out a call for possible names for the phenomena of people erroneously asserting that some ideological opponent hadn’t commented on a story without properly verifying that this was true. Between Facebook and the comments section I got a few good options, but the overall winner was set up by bluecat57 and perfected by the Assistant Village Idiot: Premature Expostulation. I have to admit, expostulation was one of those words I only sort of knew what it meant, but the exact definition is great for this situation “to reason earnestly with someone against something that person intends to do or has done; remonstrate:” Therefore, the definition for this phrase is:

Premature Expostulation: The act of claiming definitively that a person, group or media outlet has not reported on, responded to or comment on an event or topic, without first establishing whether or not this is true. 

Premature expostulation frequently occurs in the context of a broader narrative (they NEVER talk about thing X, they ALWAYS prioritize thing Y) , though it can also occur due to bad search results, carelessness, inattention, or simply different definitions of what “covered the story” means. If someone is discussing a news outlet they already don’t like or you are not familiar with, be alert.  It’s easy to miss a statement from someone if you don’t frequent what they write or don’t keep up with them.

To note, premature expostulation is a specific claim of fact NOT subjective opinion. The more specific the claim, the more likely it is (if proven wrong) to be premature expostulation. Saying a story was “inadequate” can cause endless argument, but is mostly a matter of opinion. If you say that a news outlet “stayed silent” however, showing that they ran even one story can disprove the claim.

I think there’s a lot of reasons this happens, but some of the common ones I see seem to be:

  • Search algorithm weirdness/otherwise just missing it. Some people do quick searches or scans and just simply miss it. I have speculated that there’s some sort of reverse inattentional blindness thing going on where you’re so convinced you’ll see something if it’s there that you actually miss it.
  • Attributing a group problem to an individual. I can’t find it right now, but I once saw a great video of a feminist writer who was on a panel get questioned by an audience member why she had hypocritically stayed silent on a particular issue it seems she should have commented on. It turns out she actually had written columns on the issue and offered to send them to him. Poor kid had no idea what to do. Now I suspect at the time there were feminist writers being breathtakingly hypocritical over this issue, but that didn’t mean all of them were.  Even if there were hundreds of feminist writers being hypocritical, you still should double check that the one you’re accusing is one of them before you take aim.
  • Attributing an individual problem to a group Sometimes a prominent figure in a group is so striking that people end up assuming everyone in the group acts exactly as the one person they know about does.
  • Assuming people don’t write when you’re not reading When I had a post go mini-viral a few months ago, I got a huge influx of new people who had never visited this blog. I got many good comments/criticisms, but there were a few that truly surprised me. At least a few people decided that the biggest problem I had was that I never took on big media outlets and that I only picked on small groups, or that I was never talked about statistics that might challenge something liberals said. Now regular readers know this is ridiculous. I do that stuff all the time. For whatever reason though, some people assumed that the one post they read of mine somehow represented everything I’d ever written. That’s a personal anecdote, but we see this happen with other groups as well. During the gay marriage debate I once had a friend claim that Evangelicals never commented on straight divorce. Um, okay. No. You just don’t listen to them until they comment on something you are upset by, then you act like that’s all they ever say.
  • The emotional equivalency metric If someone doesn’t feel the same way you do, they must not have seen the story the way you have. Therefore they can’t have covered the story until they mirror your feelings.

I’m sure there are other ways this comes up as well, feel free to leave me your examples.