What misused statistical terms are worth correcting? Statistics is everywhere; common usage of statistical terms is, however, often unclear.
The terms probability and odds are used interchangeable in lay English despite their well-defined and different mathematical expressions.
Not separating the term likelihood from probability routinely confuses physicians trying to quantify the probability of breast cancer given a positive mammography, “Oh, what nonsense. I can’t do this. You should test my daughter; she is studying medicine.” 
Equally spread is the use of correlation instead of association. Or correlation implying causation. 
In Al Gore's famous documentary An Inconvenient Truth, a slide illustrates the correlation of ice core $\small \text{CO}_2$ and temperatures, leaving the more technical work to prove causation out of the discussion:


QUESTION: Which statistical terms pose interpretation problems when used without mathematical rigor, and are, therefore, worth correcting?

 A: Kurtosis does not measure "peakedness." 
By definition, it is the expected value (average) of $Z^4$.* Thus, $|Z|$-values less than 1 (corresponding to data values within one standard deviation of the mean, where any "peak" would be) contribute very little to kurtosis; nearly all the contribution to kurtosis is from $|Z|$-values greater than 1 (outliers in particular).  See http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4321753/ , Figures 2 and 3 in particular.
* Subtract 3 or not; it makes no difference to this point.
A: I find abbreviations that aren't clearly indicated are a real problem.  For example, I see things like GLM and nowhere is it specified if this means general linear model or generalized linear model.  Once can usually figure out what is being referenced after digging into the context, but I find this is particularly troublesome for students just starting to learn about statistical models.  
Another example of this is IV.  Does this mean instrumental variable or independent variable?  Often times it's not made clear until you examine the context.
Something else I see confusion over are "moderator" and "interaction."  Also, population (as in the population in general) and the population of interest seems to confuse new students unless it is made very clear.
A: One that is common in everyday language: 
average 
To the average person out there (bitter irony fully intended), the mean, median, mode and expected value of anything seem to be the same. They have a natural tendency to do a point estimation, with the unconscious and unassailable assumption that there is an underlying normal distribution. And the equally unconscious assumption of a very small variance. The belief that such an estimation 1) exists and 2) will be very useful for them, because they can take it as a practically certain predictor, is so ingrained, that it is basically impossible to convince them otherwise. 
For a real-world example, try to talk to a cook who is asking "what is the average size potato", absolutely certain that if you tell him a number, he will be able to use this one for any recipe which specifies a number of potatoes, and have it come out perfect every time. And getting angry at you for trying to tell him "there is no such number". Sadly, it happens in situations with much higher stakes than making soup. 
A: Linear means:


*

*Line-like. As in $y = a + bx$ from elementary algebra. In this respect nonlinear means things like $y = a + bx + cx^{2}$, and also things like $y = ax^{b}$

*Linear in the parameters being estimated. As in a regression model (linear, logistic, GLM, etc.) entails a sum of products of scalar parameters and independent variables. In this respect nonlinear means things like $y = \frac{e^{a+bx}}{1+e^{a+bx}}$, and $y = a + bx +x\max (x-\theta,0)$.

*Linear meaning the opposite of dynamic. As in whatever a dependent variable is a function of, it is not a function of its own previous values. In this respect nonlinear means things like $y_{t} = a + by_{t-1} + cx$, and $y_{t}-y_{t-1} = a + b(y_{t-1} - x_{t-x}) + c(x_{t}-x_{t-1}) + dx_{t-1}$.
Where $y$ is a dependent variable, $x$ is an independent variable, and $a, b, c, d$, and $\theta$ are parameters in all the above examples.
A: The question was about uses of statistical terms that we should CARE to correct. I have been correcting my millennial kids' use of the term 'random' to mean things that are the opposite of random for 10 years now. Considering how many of my trainees struggle to produce a random data sample that is actually random, which happened even before this use of the word, the obfuscation of this term in everyday slang is a crisis.
From the OnlineSlangDictionary:


Definition of random


random

adjective


  
  
*
  
*unexpected and surprising.
  
  
All of the sudden this guy jumped out from behind the bushes, it was so random!
The street cleaner never comes down our street. How random.


  
*unexpectedly great.
  
  
The party was totally random.



A: There are already too many great examples mentioned by Glen and Nick... not much left!
Some aspects of regression


*

*error term and residual (it is somewhat funny when people are proud their residuals are uncorrelated with the regressors)

*prediction and estimation (should we even stop making the distinction when they are about the predicted random effects?)

*prediction/forecast interval versus confidence interval. I think there is a probability > 0.5 to quote the wrong one. 

*regressor (column in the design matrix) versus covariable et al. Especially in technical situations where the distinction is essential, many people (including myself) tend to be imprecise. 
A: Fixed effects and random effects can mean different things for different people. In econometrics fixed effects are actually random and when you think about it every effect in statistics is random so naming something random does not give any meaningful additional information.
A: Some of the things I encounter:


*

*Treating significance level and CI coverage probabilities as interchangeable, so that people end up doing things like speaking of "95% significance". 
[What's worse is when people who make such errors point to their lecture notes -- or even textbook -- as support for this; in other words the mistake is not theirs, but is being compounded a hundredfold or many-thousands-fold, and worse, even if they understand it correctly, they may actually have to repeat the error anyway, to pass the subject.]

*There's also a common tendency to think that "significance" somehow exists outside a specific hypothesis/question (leading to questions like "are my data significant" without any clear notion of what question is to be addressed). [A related issue is the "what test should I use for these data?" as if it were the data - rather than the question to be answered - that's the driver of choice of analysis. (While the "design" of the study can impact the specific tests used, the question of interest is more important -- for example, if you have three groups available but your question of interest only relates to a comparison of two of them, the fact that you have three doesn't force you to do a one-way type analysis rather than a straight comparison of the two groups of interest ... as long as your choice of analysis doesn't derive from what the data show. Ideally you plan your questions and analyses before you have data, rather than throwing analysis at data and see what sticks, which it seems post-hoc analysis questions - including "what test should I use for these data?" - tend to lead to.) 

*An occasional tendency to refer to the complement of p-value as some sort of "confidence in", or "probability of" the alternative.

*"nonparametric data"; another one unfortunately found in a couple of books (and, sadly, in an article that purports to correct a common error) this one comes up so often that it's in my short list of automatically generated comments (which begins "Data are neither parametric nor nonparametric; those are adjectives that apply to models or techniques...") (thanks Nick Cox for reminding me of this particular bugbear)
Usually what is intended is "non-normal data" but parametric doesn't imply normal, and having approximate normality doesn't imply we need parametric procedures. Similarly, non-normality doesn't imply we need non-parametric procedures. Occasionally, what is intended is "ordinal data" or "nominal data" but in neither case does that imply that finite-parametric models are inappropriate.

*A common tendency to misunderstand the meaning of "linear" in "linear model" in a way that would be inconsistent with the use of the term "linear" in "generalized linear model". This is partly the fault of the way we use terminology.

*conflating the mean-minus-median kind of skewness with third moment skewness, and conflating a zero in either (or even both) with symmetry. Both errors are frequently found in basic texts widely used in some particular application areas. [There's a related error of treating zero skewness and zero excess kurtosis as implying normality]

*this one is so common it's becoming hard to call it an error any more (due in part to the efforts of a particular program) -- calling excess kurtosis simply "kurtosis"; a mistake pretty much guaranteed to lead to communication problems.
A: "Data" is plural.  (The singular is "datum").  
A: In insurance environments especially, it is common to use variance to refer to any sort of difference, rather than the mean of the squared differences between each data point and the mean of the data set.
A: Bayesian
Students learning it might not have trouble telling you whether something "looks" Bayesian, but ask them to solve a problem with a frequentist and a Bayesian approach and they'll probably fail.
In my experience students end up being taught that it's just a philosophical difference, with no concrete example that shows the same problem being attacked with both approaches.
Now ask them why someone might take a frequentist approach in their example; chances are their best explanation would be something like "well, back in the old days, computers didn't exist..."
A: Risk 
Risk does not mean probability
Risk is the sum of the costs of all outcomes, each of these costs multiplied by the probability of them happening.
Risk is usually weighed against reward which is the gain that we are seeking to achieve.
Here is one example: How Deadly Is Your Kilowatt. Here the risks — number of dead people for different sources of energy — are weighed against the reward — terawatt hours of energy produced by these sources of energy.
So for instance: the risk of nuclear power is not the probability that a meltdown will happen; it is the probability that a meltdown will happen, multiplied by the number of people that die from it, summed with with the number of people that die from normal operations multiplied by the probability that operations remain normal. 
A: While not strictly a statistical term, I vote to retire endogeneity.  It's used to refer to everything from reverse causation through confounding to selection and collider bias, when all people really want to do is say: 
"That effect is not identified".
A: Percent vs. Percentage Points:  If something increases from 1% to 2%, it increased by 100%.  Or: you can say that it increased by 1 percentage point.  
Stating that the increase was 1% is very misleading.
A: "Regression towards the mean" does not mean that if we've observed a certain number of iid samples below expected value, the next iid samples are likely to be above the expected value.
A: It can be futile to fight against shifts in language. But
parameter does not mean variable
In classical statistics, which in this case starts precisely with R.A. Fisher who first used the term with this meaning, a parameter is an unknown constant to be estimated, say a population mean or correlation. In mathematics, there are related but not identical meanings, as when a curve is given parametrically. In many sciences, parameter is just another word for a measure (itself a term dense with mathematical meaning), property or variable, say length or conductivity or porosity or virtue, as the case may be. Naturally, an individual's length or virtue is unknown before it is measured. but statistically minded people can be bemused by its use for a set of such measurements. In ordinary or vulgar parlance, parameters (almost always plural) often mean the limits of something, say a personal relationship or a political policy, perhaps stemming from some original confusion with perimeter. With high prior probability it is to be presumed that Bayesians will speak for themselves on their own usages (grateful nod to @conjugateprior).
skewed does not mean biased
For a century or more, skewness has had a specific statistical sense of referring to asymmetry of distributions, whether assessed graphically, measured numerically, or presumed theoretically as a matter of faith or hope. For much longer, or so it may be guessed, bias has meant being wrong on average, which -- so long as we know the truth, meaning a true or correct value -- can be quantified as systematic error. Skewed in ordinary language has a common sense of being warped or distorted, and thus of being incorrect, wrong and so also biased too. That sense (so far as I have noticed, only quite recently) has begun filtering back into statistical discussions, so that the original meaning of skewness is in some danger of being blurred or submerged.
correlation does not mean agreement
Correlation has attracted several precise senses in statistics, which have in common an idea of a bivariate relationship perfect in some precise sense: the leading cases are linear and monotone relationship. It is often diluted, even in statistical discussions, to mean almost any kind of relationship or association. What correlation does not mean, necessarily, is agreement: thus $y = a + bx$ implies Pearson correlation of  $1$ or $-1$ so long as $b \ne 0$, but agreement $y = x$ requires the very strict condition $a =0, b= 1$.
unique does not mean distinct
It is quite common to talk about the distinct values of data as unique, but unique is still ideally better preserved as meaning occurring once only. My own guess is that some of the blame stems from the Unix [sic] utility uniq and its imitators, which reduce possibly repeated values to a set in which each value really is unique. The usage, on this guess, conflates input and output of a program. (Conversely, if we talk of duplicates in data, we rarely restrict ourselves to doubletons that occur precisely twice. The term replicates would make more sense linguistically but has been
pre-empted for deliberate replication of  controls in experiments; the resulting response values are usually not at all identical, which is much of the point.)
samples are rarely repeated
In statistics, a sample includes several values, and repeated sampling is a high theoretical virtue, but one rarely practised, except by simulation, which is our customary term for any kind of faking in silico. In many sciences, a sample is a single object, consisting of a lump, chunk or dollop of water, soil, sediment, rock, blood, tissue, or other substances varying from attractive through benign to disgusting; far from being exceptional, taking many samples may be essential for any serious analysis. Here every field's terminology makes perfect sense to its people, but translation is sometimes needed.
error does not usually mean mistake; as Harold Jeffreys pointed out, the primary sense is erratic, not erroneous.
Nevertheless, we should be wary of our own sins or quirks of terminology:
expected values or expectations (for means over the possible outcomes) may not be what you expect at all, and could even be impossible: in tossing a die fairly with outcomes 1 to 6, the expected value is 3.5
regression is not going backwards
stationary does not mean immobile or fixed
confidence has nothing to do with anyone's mental or psychological state
significance has only sometimes its everyday meaning
exact is often an honorific term, referring to a conveniently tractable solution or calculation rather than one appropriate to the problem
right-skewed distributions to many look skewed left, and vice versa (and the terminology of right and left for skewness assumes that you are looking at something like a conventional histogram, with horizontal magnitude axis)
the lognormal is so called because it's an exponentiated normal
but the lognormal is more normal than the normal
the Gaussian was discovered by De Moivre
Poisson didn't discover the Poisson, let alone Poisson regression
the bootstrap won't help you with your footwear
the jackknife doesn't cut
kurtosis is not a medical condition
stem-and-leaf plots don't refer to plants
a dummy variable is useful, not pointless or stupid
who on Earth (or anywhere else) thinks that heteroscedasticity is really a preferable term over  unequal variability?
robust now has at least two major technical meanings for different groups, neither of which inhibits its frequent use, even in technical discussions, to mean merely something like "asserted to behave well"
IV now has at least two major meanings for different groups
factor now has at least two major meanings for different groups
normalize and standardize have uncountably many meanings (we really need to standardize there)
versus describing a graph means vertical variable versus horizontal variable, unless it means the opposite
and (last but not least, to coin a phrase) statistics has at least three major meanings.
Notes:

*

*Despite any appearances to the contrary, I think this is a good, serious question.


*Fashions shift. Well into the twentieth century, it seems that many people (no names, no pack-drill, but Karl Pearson could be mentioned) could only invent terms by reaching for their Greek and Latin dictionaries. (It would be unfair not to give him credit for scatter plot.) But R.A. Fisher did hijack many pre-existing English words, including variance, sufficiency, efficiency and likelihood. More recently, J.W. Tukey was a master in using homely terms, but few should feel distress that sploms and badmandments did not catch on.


*One comment is based on recollection of "Life is [...] Multiplicative rather than additive: the log normal distribution is more normal than the normal." Anon. 1962. Bloggins's working rules. In Good, I.J. (Ed.) The scientist speculates: an anthology of partly-baked ideas. London: Heinemann, 212-213 (quotation on p.213).
