- Do not speculate in your abstract. Abstracts are the place to report what you did, why you did it, and what you found. It is fine to report any conclusion that follows directly from your data. But, you should not use the abstract as a place to make claims that exceed your evidence. For example, even if you think your findings with undergrads in your laboratory might be relevant for a better understanding of autism, your abstract should not mention autism unless you actually studied it. Readers of your abstract (often the only thing people read) will assume that what you said is what you found, and media reports will focus on that your speculation rather than your findings.
- Separate planned and exploratory analyses and label them. If you registered your analysis plan and stick to it, you can mark those analyses as planned, documenting that you are testing what you originally intended to test. It is fine to explore your data fully, but you should flag any unplanned analyses as exploratory and note explicitly that they require replication and verification. Your exploratory analyses should be treated as speculative rather than definitive tests.
- Combine results and discussion sections. Justify each analysis and explain what it shows in the same place in your manuscript. If you separate your analyses and explanations, non-expert readers will skip your evidence and focus on your conclusions. By combining them, you allow the reader to better evaluate the link between your evidence and your conclusions.
- Add a caveats and limitations section. In your general discussion, you should add a description of any limitations of your study. That includes shortcomings of the method, but also limitations to the generalizability of your sample, effects in need of replication, etc. If your effects are small, you should note if and how that limits their practical implications. By identifying limitations and caveats in your paper, your readers will better understanding what your findings do and do not show.
- Specify the limits of generalization. Few papers do this, but all of them should. Most papers in psychology test undergraduates and then make claims as if they apply to all of humanity. Perhaps they do, but any generalization beyond the tested population should be justified. If you tested undergraduates and expect your studies to generalize to similar undergraduate populations, you should say so. If you think they also will generalize to the elderly or to children, you should say so and explain why. Spell out the characteristics of your sample that you think are essential to obtain your effect. Specifying generalization has benefits. First, it lets readers know the scope of your effects and helps them to predict whether they could obtain the same result with their own available population. Second, it clarifies the importance of your findings. If you expect that your effects are limited to subjects at your university in December of 2012 and won't generalize to other times or places, then it is less clear that anyone should care. Third, by specifying your generalization, you are making a more precise claim about your effect that others can then test. If you claim your effect should generalize to all undergraduates, then anyone testing undergraduates should be able to find it (assuming adequate statistical power), and if they can't, that undermines your claim. If you restrict generalization too much to protect yourself against challenges, then others will have no reason to bother testing your effect. Perhaps most importantly, if you appropriately limit your generalization in the paper itself, then media coverage will be less likely generalize your claims beyond what you actually intended.
- Flag speculation as speculation. If you must discuss implications that go beyond what your data show, explicitly flag those conclusions as speculative and note that they are not supported by your study. By calling speculation what it is, you avoid having others assume that your wildest and most provocative ideas are evidence-based. Speculation is okay as long as everyone reading your paper knows what it is.
Bonus suggestion: If you have a multiple-author paper, the Acknowledgements or Author's Note should specify each author's contributions clearly and completely. By doing so, you assign both credit and blame where it is deserved. For example, when I collaborate on a neuroimaging project, I make clear that I had nothing to do with any of the imaging data collection, coding, or analysis. I should get no credit for that part of a study (given that I know nothing about imaging), but I also should take no blame for any missteps in that part of the project.