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In this article, the authors address the practicalities of how data should be presented, summarized, and interpreted. There are no exact rules; indeed there are valid concerns that exact rules may be inappropriate and too prescriptive. New procedures evolve, and new methods may be needed to deal with new types of data, just as people know that new ingredients may require modified cooking methods. However, most writers should follow reasonable principles based on current practice, although some flexibility is required, as the authors show in this article. (Contains 2 figures.)

Statisticians use words deliberately and specifically, but not necessarily in the way they are used colloquially. For example, in general parlance "statistics" can mean numerical information, usually data. In contrast, one large statistics textbook defines the term "statistic" to denote "a characteristic of a "sample", such as the average score", and the word "parameter" to denote "a characteristic of a "population"." However, for statisticians, statistics means more than just numerical information or a characteristic of a sample. Statistics is also a discipline. It is relevant to all areas of scientific enquiry, and spans study design, data collection, developing methods of analysis and analysing data, interpreting results, and making predictions. Other words that statisticians use carefully, and very particularly, form the cornerstones of statistical reasoning, words such as probability, significance, likelihood. Why does this matter? Well, it may well explain why non-statisticians struggle with the ideas and concepts used by statisticians. The reader is confused by the terminology, not the least because the statistician uses familiar words in different ways, a jargon similar to other professional vocabularies. This necessary precision of language becomes evident when considering one of the statistical methods--"the t test"--commonly used by biological scientists today. There are different ways to look at data. Some provide useful alternatives for scientists. For example, non-parametric tests could be considered. Another possibility may be permutation tests. A natural extension to significance testing is estimation and, in particular, the use of confidence intervals, where a correspondence with significance testing can be made. In this article, the authors describe how Student used statistics to measure the quality of the product at the brewery where he worked. (Contains 3 figures.)

How effective is training frogs to jump? This is perhaps the most frequent question in biology that is subjected to statistical analysis: does a treatment make a difference? One can examine whether there is indeed a training effect, by first assuming the opposite. That is, the authors assume that training has no effect on the mean distance jumped. If they propose that the mean values have been calculated from samples taken from the same population, any difference they do find between the mean values would be the consequence of chance alone. They calculate the probability (or P value) of finding the observed difference between the mean distances jumped by the two groups, or something more extreme, given that they are taken from the same population. The authors have ignored, in the last experiment, the fact that they have carried out several previous experiments. Each time they did an experiment, they accepted that there was a 5% chance of a false positive result (i.e. they would conclude there was a difference, when in fact there wasn't one). This may increase the risk of concluding, purely by chance, that a difference exists when there actually is none: the more tests, the greater the risk that a false positive will occur. (Contains 5 figures.)