Psychology has almost ruined some perfectly innocent words. In each case, the first step was to take a useful word from the English language and give it a technical meaning that did not exactly or, in some cases, even approximately, match what it meant to begin with. This is OK as far as it goes; technical terms are useful and have to come from somewhere. But the second step, which is taken all too often, is to forget that this was done, and act as if the word still had some or even all of its original meaning. The result: widespread confusion. Examples, starting with the most obvious:
What it originally meant: sufficiently great or important to be worthy of attention; noteworthy.
How psychology uses the word: As used in “significance testing,” this word actually, and merely, means not-random. The most succinct – and accurate – interpretation of the meaning of a “significant finding” that I’ve seen is “there’s not nothing going on.”
Why it’s a problem: An undergraduate psychology student, having just taken a stats course, phones home one evening and says, “Mom, something significant happened today!”
Mom: “Oh my goodness, Sweetie, what do you mean?”
Undergraduate: “I mean, there’s less than a 5% chance that what happened was completely random!!!”
Mom: [hangs up]
Of course, this is a hypothetical situation as well as an (admittedly weak) joke. However, it does exemplify the way that “significant” findings are often interpreted and reported as if they are actually important – which is a different matter altogether (as our undergraduate’s mom seems to know).
Suggested improvement: How about we call statistically “significant” findings non-random? As in, “the difference in means had a low probability of being random (p < .05).” Whether the finding is actually significant is a different issue that requires further discussion. A lot of further discussion.
What it originally meant: Put right (an error or fault).
How psychology uses the word: To remove one or more sources of variation from a score, as in, “we corrected the scores for gender, SES, and health status.” Or (and this is a real example), “before computing agreement between spouses on their ratings of marital quality, we corrected them for the overall rating of the quality of their marriage.”
Why it’s a problem: The use of the word “correct” (as a verb) implies in ordinary English that the score used to be wrong, but now it’s right. So, it sounds good to be able to say you’ve “corrected” your scores, because now they must be, well, correct. As a result, things get “corrected” that shouldn’t be, as in cases where the source of variation being removed is an essential part of what is being measured (as in the example just mentioned), and/or when the “corrected” score has had so much of its measurement meat removed that what remains is little more than random noise.
Suggested improvement: Call “corrected” scores “adjusted” scores, instead. This invites the reader to consider whether the adjustment was justified in this case, rather than encouraging the assumption that a number that was previously wrong, has been put right.
Bonus improvement: Quit using partial correlations so much. When you do use them, always also report the zero-order (non-partialled) correlation.
Extra bonus improvement: Read your Egon Brunsik about the pitfalls of experimental as well as statistical control (Brunswik, 1956).
What it originally meant: Consistently good in quality or performance; able to be trusted.
How psychology uses the word: In at least four ways. (1) Test-retest reliability; Will the person get (close to) the same score on two different occasions. (2) Alternative forms reliability; Will two different versions of a measure give a person (close to) the same score. (3) Internal reliability: Do the items of a measure correlate with each other, thus indicating that they tap into the same underlying (latent) factor. (4) Inter-judge (or inter-rater) reliability: Do two (or more) raters (or judges) agree with each other.
Why it’s a problem: Notice the distance between the dictionary definition and any of its four psychological usages. A reliable measure might not be valid (students are understandably confused the first time they are taught this distinction), and the word sounds so virtuous that (I have the impression) even psychologists have more faith in a “reliable” measure than they should, based on its “reliability” alone.
Suggested improvement: Divide into at least four terms for the usages listed above. Respectively: “test-retest consistency,” “alternative forms similarity,” “item convergence,” and “inter-judge agreement.” And when aggregating items, scores or ratings, namethe exact statistic, as in “the (Cronbach’s) alpha was…” “Alpha reliability” was always redundant anyway.
Variance (vs. Variation) (n.)
What it originally meant: the fact or quality of being different, divergent, or inconsistent.
Compare this with the definition of the rarely-used term,
Variation: a change or difference in condition, amount, or level, typically with certain limits.
How psychology uses the word: The word “variance” typically is used to identify what we hope to “explain” (see next entry) with our theories and statistical models. It’s the average of the sum of the squared – let me repeat that, the squared – deviations of the observed scores from their mean.
Why it’s a problem: First, look at the two definitions. Which word better describes what we want to explain with our psychological theories and statistical models? In my opinion, it’s the second word, the one we don’t use. Second, look at what the widely used-term reflects. Squared deviations are computationally convenient and appropriate for certain uses, but also change the scale of the unit being studied and are potentially distorting. For example, Indianapolis is (approximately) 1000 miles from Boston and 2000 miles from Los Angeles, which means that when you are flying from Boston to LA you are 1/3 of the way there when you get to Indianapolis. You are also 1/9 of the squared distance from Boston to LA. Which of these numbers is more meaningful, or useful for purposes of, say, calculating travel time or jet fuel consumed? Third, as the distinguished cognitive psychologist Robert Abelson once pointed out, presenting results in terms of the amount of variance explained can be misused to “highlight the explanatory weakness of an investigator’s pet variables” (Abelson, 1985, p. 129). In other words, and despite the many attempts to correct the underlying misconceptions, it can still be a devastating critique to say, for example, “your results (r = .30) only explain 9% of the variance.” Yeah, but they also explain 30% of the (unsquared) variation.
(For further development of this point see Ozer (1985), Funder & Ozer (2019).)
Suggested improvement: Except in technical contexts where (squared) variance must be used for computational purposes, characterize variation in scores in terms of absolute value of the original units. This is usually called M.A.D. (mean absolute deviation). But my suggestion is to instead use ordinary word “variation” to describe this… well… variation.
Bonus improvement: Stop mindlessly squaring r’s, for pity’s sake (Ozer, 1985).
What it originally meant: Make (an idea or situation) clear to someone by describing it in more detail or revealing relevant facts.
How psychology uses the word: Solely in a narrow, statistical sense, to refer to the part of the variance accounted for by another variable via a statistical model (e.g., analysis of variance, or regression).
Why it’s a problem: Psychology is in the business of explaining stuff, or trying our level best to do so. The computation of deviations (squared or not) and accounting for the source of the deviations, as a result of an experimental manipulation and/or its correlation with another variable, is part of this process. But it’s just the beginning, and the word “explained” is too pretentious a label for this data analytic step. Usage of this word also directs our attention towards “explaining” variance, as if that’s all we need to do, rather than explaining (without scare quotes) what’s really going on.
Suggested improvement. Reserve the word “explain” for when you are really explaining something. Like, with a theory. If what you are doing is just accounting for numbers, use the term “account for” instead. (Even that sounds like a bit much to me, but if it’s ok for accountants, I guess it’s ok for me.)
What it originally meant: say or estimate that (a specified thing) will happen in the future or will be a consequence of something.
How psychology uses the word: Pretty loosely, to refer to any correlation between variables that allows the value of one variable to be estimated from the value of the other.
Why it’s a problem: Maybe it’s not a big problem, but it is kind of misleading, isn’t it? Actual “prediction” isn’t done very often in basic research. I’ve seen – I’ve been guilty of writing – articles that say behavior or some other outcome can be “predicted” by a personality variable, when no actual predictions, for any particular individual are being made or ever intended to be made. Applied work is different; among the most robust findings in personality psychology is that conscientiousness (and related traits) can predict job performance, and some of our colleagues are earning a good deal of money doing just that.
Suggested improvement: Use terms that don’t imply quite so much precognition, such as “correlated with” or “associated with” when no actual predictions are being made. Save “prediction” for contexts, such as industrial or medical settings, where an estimate of something that will happen in the future is actually being based on something that was measured today.
What it originally meant: a mistake.
How psychology uses the word: In two ways. First, “error” refers to unaccounted-for (trying to follow my own advice here) variation (ibid.) in an observed variable over cases, over time, or within experimental conditions. Second, in the study of judgment, “error” refers to any deviation in human judgment from the output of a normative model, which might be anything from set inclusion logic to Bayesian statistics.
Why it’s a problem: Both uses of error are potentially misleading, but the consequences seem more dire in the second usage. In the first case, occasionally one sees lay summaries of research (e.g., in the media) that gets confused by the word “error” and seems to think that if there is some in the data, then the conclusions of the study are a mistake. In the second case, and far worse, a whole generation of cognitive social psychologists came to write as if, and spread the idea that, human judgment is fundamentally characterized by woeful shortcomings. The research on which this idea was based showed that in certain experimental circumstances humans can be induced to produce judgments that deviate from the calculations of a putatively normative model. However, the degree to which such models are realistic or even normative outside of a very narrow range of predefined conditions is, shall we say, a matter of controversy. As I wrote once, errors (as psychologists have historically often used the term), are not necessarily, nor probably even usually, mistakes (Funder, 1987).
Suggested improvement: Just stop using the word “error” except in the rare cases where you have incontrovertible grounds to ascribe a mistake. Possible substitutes for the first usage: random variation, noise. Possible substitute for the second usage: deviation from the prescriptive model.
What it originally meant: a person’s essential being that distinguishes them from others, especially considered as the object of introspection or reflexive action.
How psychology uses the word: Usually, as part of a hyphenated label that denotes any of a large number of research areas, each of which has its own, sometimes sizable literature; such as: self-esteem (the winner by far), self-efficacy, self-awareness, self-determination, self-discrepancy, self-control… the “Wiktionary” (an online resource I’ve only recently discovered) lists 383 English words prefixed with self. Not all of these are areas of psychological research, but my goodness, an awful lot of them are.
Why it’s a problem. Two reasons. First, the many areas of inquiry labeled with self-hyphen are only loosely related to each other, if that, which means the “study of the self” doesn’t have much if any central meaning. Second, the actual topic of the “self” is potentially very interesting – what is the essential core of a human’s individuality? But – apart from among a few lonely humanistic psychologists and fans of William James – this core topic is among the elephants in the room that psychology ignores as it focuses, instead, on the many and more limited self-hyphen topics.
Suggested improvement: Reserve the term “self” for discussions of an individual’s essential being that distinguishes them from others. Substitute the term “own” for more limited uses, as in “own esteem,” “own efficacy,” “own perception,” etc. Warning: it is almost certainly much, much too late to implement this suggestion or, if I’m being realistic, any of the others.
Even if the improvements suggested here have no hope of taking hold – which I think is a realistic expectation – I do hope this little essay might help make us a little more thoughtful every time we hear or use these 8 pesky words.
Abelson, R.P. (1985). A variance explanation paradox: When a little is a lot. Psychological Bulletin, 97, 129-133.
Brunswik, E. (1956). Perception and the representative design of psychological experiments. Berkeley: University of California Press.
Funder, D.C. (1987). Errors and mistakes: Evaluating the accuracy of social judgment. Psychological Bulletin, 101, 75-90.
Funder, D.C., & Ozer, D.J. (2019). Evaluating effect size in psychological research: Sense and nonsense. Advances in Methods and Practices in Psychological Science, 2, 156-168.
Ozer, D.J. (1985). Correlation and the coefficient of determination. Psychological Bulletin, 97, 307-315.
 Source of this and other definitions: Lexico (Oxford) online dictionary.
 Or, more precisely, “a low probability of arising by chance if there really is no difference,” but that seems kind of wordy.