Communicating original research findings well requires efficiently telling the factual story that the researchers wish to convey, as well as the speculations that the authors wish to propose. It is the first author who needs to organize the thinking of all the other authors into a manuscript that is coherent, worthwhile, and efficient.

We recommend submitting for English editing after you are comfortable that you have fully, logically, and efficiently presented the messages that you have carefully decided that you wish to present, and after deleting any sentences and paragraphs that are not essential to conveying the most important messages.

In this blog post and the one that follows, I offer 30 thoughts and suggestions that, taken together, should improve the ability of your manuscript to communicate the important results of your original research efforts. The second blog post ****LINK***** will focus on the Introduction, Discussion, and Abstract. For now, this first blog post presents general considerations and specific comments regarding the Methods and the Results section of a manuscript. I hope you will read them both and that you and your manuscripts benefit from them.

1. The purpose of a manuscript should not to be to get something published in order to fill ones curriculum vitae and improve chances of being promoted within academia. Instead, a manuscript’s purpose is to convey important scientific findings in a manner that others can use to improve the human condition. A well-written manuscript can not only gain attention for exciting novel findings, but it can also prevent wasteful repetition of research efforts, as well as inform others of mistakes to avoid. One should only write manuscripts that are worth writing and that will help others move science forward.

2. Shorter is always better. It takes more time to write a good short article focused on your message than it does to write a long article filled with noise. But it is worth the time to remove the noise and leave the message concisely stated.

3. If the study was hypothesis driven, state the hypothesis in the introduction, and possibly in the abstract. Then make sure that everything you put in the manuscript is in some way contributing to the process of testing of discussing that hypothesis. If it doesn’t relate to the hypothesis, remove it. If it does relate to the hypothesis, make sure the reader is told exactly how it relates.

4. If the study identified unexpected findings outside the hypothesis, then admit this in the introduction so that the readers will know it is a serendipitous finding that you are presenting. Readers and reviewers will forgive a less than perfect study when it was not originally designed to examine the unexpected observation.

5. Before embarking on any research, make sure the study design can confidently answer the questions of the hypothesis. If the study design is flawed, the manuscript will be poor no matter how well it is written and edited.

6. If the study is not hypothesis driven, make that clear in your mind. And then also convey that message in the manuscript.

7. Before you start writing the manuscript (assuming a hypothesis-driven project), write down your hypothesis. The hypothesis is the anchor that will keep your mind from drifting off topic. Next, make a list of the messages you wish to convey to the reader. All the messages should relate to the hypothesis, although they don’t all have to support it. Organize the list in order of importance or in some other logical order. What do you want the reader to most confidently remember from all the work you have done? Those are your important messages. Each important message in your list will then serve as the first sentence of sequential paragraphs in the Discussion that you will write later (and that will be discussed in the next blog post). Often 2-5 key messages are identified, but there can be more or less.

8. I do not recommend writing the manuscript in the order it will be read. Instead, write the Methods section first, with all the detail that is necessary for others to replicate the study. For each paragraph of the Methods section, re-read your hypothesis and remember why you did the experiment. If you are not thinking about the connections to the hypothesis, it will be difficult for the reader to do so. You can fine tune your Methods section later.

9. It is often helpful to add a phrase to each Method description consisting of a few words explaining why this method was performed and how it links to other experiments in the project. For example, “To confirm our mRNA evidence, we analyzed protein products using…..” Or, “To further evaluate the utility of psychometric testing in ADDH diagnostics, we performed long-term outcomes assessment by means of…”

10. Statistical methods. Many readers will simply trust the authors to make sure that the appropriate statistical tests are used for analysis. As such it is important that the authors choose the correct statistical analytic methods and understand them. Getting help from a statistician is wise.

11. Next, prepare to write the Results section in a sequence of paragraphs that are entirely logical. Reread your hypothesis. Connect in writing each reported finding to the hypothesis that you are testing.

12. Interpretation of the results of the experiments within the Results section is sometimes done, but more often is reserved for the Discussion. If you do explain your findings in the results section, you have to stick to FACTS only, and don’t then repeat yourself in the discussion. Refrain from speculating in the Results section. Speculation (using words such as “maybe”, “perhaps”, “possibly”, “might”, “potentially”) is reserved for the Discussion.

13. In the Results section, present the facts in as efficient a manner as possible. If a table or figure is the easiest way to state the results, rely on the table and figure. It is totally acceptable to state, “The results of our serum lab assays are shown in Table 1.” It is also okay to add to this, “Note the marked elevations in 5 out of the 12 analytes assayed.” It is much easier for a reader to examine the table or figure than to try to decipher a kilometer long paragraph full of data in the form of prose. Results sections may have very few words if there are excellent tables and figures.

14. Data—both in the text and in tables—should be presented without any insignificant digits. Essentially, don’t provide numbers after the decimal if those numbers are not truly important in the real world. There is very little point to comparing 456.237 +/- 64.243 with 234.549 +/- 76.345! 456 vs 234 is just fine, no matter what variable is being examined. Indeed, using excessive and insignificant digits is an indicator to reviewers that the authors are not thinking about the real meaning of their data, but rather are stuck focused on mathematics and statistics.

15. Avoid being tyrannized by p values. I will use an extreme example. Would you say that an intervention was successful if the p value is 0.049, but have considered it a complete failure if the p value had been 0.051? You shouldn’t. In reality (outside of arbitrary statistics) there is essentially no true difference between a p value of 0.049 and a p value of 0.051. And we should make no leaping conclusions based on which side of an arbitrary value of 0.05 that the p value falls. 0.05 is not a magic number. This is one reason why p values should always be reported as p=###, instead of just p<0.05 or p>0.05. The actual numbers provide the reader with an opportunity to see how likely the means are actually different from each other. Also, using a lot of zeros is not often helpful. p<0.0000001 looks silly in most cases, unless there are thousands of variables being considered in one analysis. p<0.001 is sufficient in almost all typical manuscripts to convey the message that differences in the data set are likely to result from actual reality.

16. Figures and Tables. Think about what a reader would want to see. Craft titles and axis labels clearly. Make font sizes as large as possible because the graphs will be shrunk. Recognize that dot plots—particularly for medical articles—are often much more informative than bar graphs. Dot plots show individual data, and patients are individuals. It is the very rare case indeed in which a patient should be managed clinically based on a bar graph of mean data in a research study. Mean data don’t tell the whole truth, because individual people are rarely average.

17. Figure legends should convey results and interpretations, without the reader having to read the manuscript. The first part of a figure legend should state the main message you wish to convey with the figure. Some journals will not want interpretation included in figure legends, but most won’t care.

18. Now go back to the Methods section and make sure that anything you reported in the Results section has appropriate methods sections supporting it. Add to the Methods whatever you have forgotten, and delete from the Methods anything that you realize wasn’t actually part of this set of experiments. Usually this is not an issue, but we do occasionally find whole paragraphs of methods that were actually never used in the study.