The Final Checklist

Your study is done, the sections are written, the interpretation is calibrated. One task remains, and it's the one most students skip: the consistency pass. Graders and reviewers rarely re-run your analysis. They can't. What they can do, in seconds, is check whether your own document agrees with itself. Does the sample size in the abstract match the method? Do the degrees of freedom fit the design? Is every figure referenced, every citation listed? An internal contradiction is a red flag that something deeper may be wrong, and it's the easiest kind of error to catch and to lose marks for.

Do the numbers match everywhere?

A single result appears in several places: the abstract, the results sentence, maybe a table, maybe the discussion. Every one of those copies must be identical. An abstract that says r = .28 while the Results report r = .31 tells the reader that at least one number was typed from memory, and now they doubt all of them. The same goes for sample sizes, means, and percentages. Pick the true value once, then make every mention agree.

Your degrees of freedom tell on you

Degrees of freedom are a quiet audit of your design. For an independent two-group t-test, df = N − 2; for a one-way ANOVA, the denominator df = N − k; for a correlation, df = N − 2. So if you report two groups of 60 but write t(116), the arithmetic doesn't close: 60 + 60 − 2 = 118, not 116. A grader who knows the formula reads your df straight back to your n, and a mismatch signals either a typo or a misunderstanding. Check that every reported df is consistent with the sample size and design you described.

The cross-reference checks

Three quick sweeps catch most of the rest:

• Every table and figure is referenced in the text ("as Figure 1 shows…"), and every one you reference actually exists. An unreferenced figure is either clutter or a broken promise.
• Every citation is in the reference list, and every reference is cited. A name in the text that's missing from the list (or vice versa) is the classic reference-manager slip.
• Every statistic is complete: test, degrees of freedom, the statistic's value, an exact p, and an effect size, with p floored at p < .001, never p = .000 (see Reporting Statistics in APA Style).

Two of those sweeps are automated now

The p-value check and the mean-plausibility check are mechanical enough that software does them, and increasingly software does them to you before a human reads a word. statcheck reads a manuscript, pulls out every result written in APA form, recomputes p from the test statistic and its degrees of freedom, and flags the pairs that disagree. Several psychology journals, Psychological Science among them, now run it as part of peer review. The survey that prompted it looked at more than 250,000 p-values across roughly 16,700 articles: 49.6% of articles reporting null-hypothesis tests contained at least one inconsistent p, and 12.9% contained one large enough to flip the significance decision. Just under a tenth of all reported p-values did not match their own statistic.

You can run that check on yourself without installing anything. Paste a result into the APA results formatter; it recomputes p the same way and says so when the value you typed and the value your statistic implies disagree.

GRIM does the equivalent job for means. When a measure is a whole number for each person (items recalled, a sum of 1-to-7 ratings), the mean of n people has to be some whole number divided by n, which leaves most decimals unreachable. At n = 20 the possible means step by 1/20, so a reported 4.32 could not have come from that sample: 4.32 × 20 = 86.4, and nobody scored four tenths of an item. The test only bites when the sample is small relative to the decimals reported. The mock paper below gives recall means of 28.4 and 24.1 from groups of 60, and both are perfectly reachable (1,704 and 1,446 items), so GRIM has nothing to say about them. That is the ordinary outcome, worth seeing once so that a clean pass doesn't read as the check having failed.

Neither tool says anything about honesty, and both find far more typos than misconduct; fraud and self-correction covers the case where they are pointed at someone else's paper. What they do for you is finish the arithmetic half of the final pass in a couple of minutes, which leaves your attention for the half no software can take over: whether the sentence you wrote is the sentence your data will support.

Find the six inconsistencies

Here is a one-page paper in miniature — abstract, method, results, a table, a figure, and references. Six internal inconsistencies are hidden in it: numbers that disagree, degrees of freedom that don't fit, a display that's never referenced, a citation that's missing. Read like a grader and click each problem. Some things you click will turn out to be fine. That's part of the job too.