Good practices for data presentation in LWT-Food Science and Technology

This article aims to guide potential authors on appropriate analyses and clear presentation of collected data, avoiding some of the most common flaws encountered during my period as an Associate Editor for LWT-Food Science and Technology. It covers statistics, use of correlations and factorial analysis, and some miscellanous data presentation issues. Its aim is not to tell authors who want to publish in LWT-Food Science and Technology - or other highly ranked Food Science journals - how they should design their research, but to help them to produce the best possible articles, and improve usefulness of these articles to the community. Its take home message is two-fold: before you start the experiments, think about the hypothesis you want to prove (or disprove) to choose the best tools, and when you have the results, look at them, not only at the statistics.

Automated summary

This article aims to guide potential authors on appropriate analysis and clear presentation of collected data, helping them to improve the quality of their articles and increase their value to the community. It suggests that authors should carefully consider the hypothesis they want to prove or disprove before starting experiments, and pay attention to the data rather than just focusing on statistics when interpreting results.