This short editorial is the first of a planned series to guide the handling of data from laboratory experiments. We hope that authors (and editors) may find this helpful in writing and assessing future journal articles. It's clear that data handling in science needs to be improved, if only from the number of attempts that have been made to rectify the problem. Books, articles and websites give copious words of advice, too numerous to cite. Many are indeed helpful, but others are often too dense and discouraging for the non-specialist. Often the advice is ‘too general in nature, too limited in scope, and too specialized in vocabulary to be useful to most authors and editors’ (Lang & Secic, 2010). On one occasion the American Physiological Society gave specific advice (Curran-Everett & Benos, 2004). When the guidelines were followed up, the results were found to be mixed, with some praise but also criticism and controversy, with little overall effect on the quality of publication (Curran-Everett & Benos, 2007). In general, encouraged by the slow but substantial change that has occurred in medical journals, there seems to be a mood to improve, although changes in basic science journals have been slow, if they have occurred at all. Comparison of basic science reports with clinical studies is not flattering (Watters & Goodman, 1999). We are well aware that advice alone has been ineffective. We could easily list a comprehensive set of guidelines, which would probably suffer a fate similar to those that have gone before. In contrast we hope that by keeping the articles in this series short and focused, they will offer advice that is digestible and palatable. They may even attract readers who find such topics difficult and unappealing when they are presented in larger portions. We shall try to avoid technical terms and complicated maths. Much advice starts with a suggestion that one should consult a statistician. Considering the time and cost of some experiments, this would often be prudent. Admittedly statisticians are a rare breed and can be hard to find. Often when the data are given to a statistician there is a problem with them that might have been avoided if the consultation had occurred at the start. Fisher once said: ‘To consult a statistician after an experiment is finished is often merely to ask him to conduct a post-mortem examination. He can perhaps say what the experiment died of’ (Fisher, 1938). In practice, it's likely that basic scientists do not know that they should seek advice beforehand, as they may not be sure what they will find, or they consider that what they will find will be easily described and analysed with a simple toolbox of tests. On occasion, physiological studies resemble a random walk through a series of ‘what ifs?’ and ‘how can we prove this mechanism?’ stages that may leave a statistician searching for a single testable hypothesis. When basic scientists do have data to analyse, they may think the exciting part of the study is done. They look around for an easy way to deal with their data, and often ask someone with possibly more experience but probably no more training what to do. The advice is often ‘well, this is what I did …’, almost designed to sustain the status quo and perhaps perpetuate error. A recent article repeated the often heard advice: ‘The choice of how to express the data is very important and should not be made solely on the basis of habit or convention. Always inspect the data in its raw form’ (Lew, 2007).