Being a statistician means being able to do several things:
Last part is perhaps the most important: you do an analysis to answer a question, and the answer to the question is the most important thing.
This is true whether you are in the corporate world, answering to a boss, or in graduate school, where you will eventually have to convince your thesis committee (and, by extension, the academic world) that what you have done is interesting, statistically sound and important.
Introduction: tell your readers about your problem and what you hope to find out. Provide enough explanation for the reader to know what you’re trying to achieve. Can also refer to what other people have done.
Methods: Where the data came from, how collected (describing technology used, if any). Scientific people call this “Methods”. Also here: describe work to get data into right form.
Analysis and results: Not enough to give analysis; have to explain what you are doing and what made you do it. Describe results in matter-of-fact way (opinions in the next section).
Conclusions: What does analysis tell you about your problem? Place results in context. Offer (supported) opinions about what the results mean, to you and the world.
Link here.
Title and authors, with journal and page numbers, so that you have everything you need to refer to it.
Journal articles typically begin with Abstract that summarizes question and gives highlights of results and conclusion, and tells you whether paper is worth your while to read.
Introduction begins with plain-English first sentence. The numbers in brackets are references to what other people have said.
The subjects. Experiments on humans require “ethical approval”.
…and
…noting that the two groups were not significantly different before the study, but changed in important respects over time. Results also shown in table.
Graph showing that bone mass density has changed greatly as a result of the jumping. (Graphs are always good.)
…
Note use of (relatively) plain English, description of most important findings, comparisons to other work, and admission of limitations.
tinytex
(R Studio on Jupyter has LaTeX already).