Correct spelling (capitalization, italicization, hyphenation) of "p-value"? I realize this is pedantic and trite, but as a researcher in a field outside of statistics, with limited formal education in statistics, I always wonder if I'm writing "p-value" correctly. Specifically:


*

*Is the "p" supposed to be capitalized?

*Is the "p" supposed to be italicized? (Or in mathematical font, in TeX?)

*Is there supposed to be a hyphen between "p" and "value"?

*Alternatively, is there no "proper" way of writing "p-value" at all, and any dolt will understand what I mean if I just place "p" next to "value" in some permutation of these options?

 A: This seems to be a style issue with different journals and publishers adopting different conventions (or allowing a mixed muddle of styles depending on authors' preferences). My own preference, for what it's worth, is p-value, hyphenated with no italics and no capitalization.
A: The ASA House Style seems to recommend italicizing the p with hyphen: p-value.  A google scholar search shows varied spellings.
A: There do not appear to be "standards".  For example:


*

*The Nature style guide refers to "P value"

*This APA style guide refers to "p value"

*The Blood style guide says:


*

*Capitalize and italicize the P that introduces a P value

*Italicize the p that represents the Spearman rank correlation test


*Wikipedia uses "p-value" (with hyphen and italicized "p")


My brief, unscientific survey suggests that the most common combination is lower-case, italicized p without a hyphen.
A: P value from theoretical point of view is some realization of random variable.
There is some standard (in probability) to use upper case letters for random variables and lower case for realizations.
In table headers we should use P (maybe italicize), in text together with its value p=0.0012 and in text describing for example methodology p-value .
A: Omitting the hyphen can sometimes change the meaning of sentences or at least they can become ambiguous. This can occur especially in papers that describe statistical tests or introduce algorithms to evaluate p-values, but one may also describe methods that have nothing to do with statistics, and still calculate p values from t tests (but not the p-values using statistical t-tests). In this kind of context, the hyphens would really be necessary, even if writers usually try to avoid notations that could get easily confused.  
Example (with a bad choice of notations): We would like to find a set of strong association patterns and evaluate the probability that the result would have occurred by chance. In the first phase, we search for the z best patterns with some goodness score. So, after the search phase, we will have z scores (but the z-scores). Then we evaluate the best patterns with a randomization test. We generate t random data sets and evaluate the score of the z:th best pattern in each data set. So, we perform t tests (but not the t-tests) and output the score of the z:th best pattern. We find out that p values (but not the p-values) of all t score values are better then the original z:th best pattern had. Therefore, we could estimate that the probability of getting z so good patterns by chance is p/t.  
