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.)