"The preparation of clear and simple plans, and a convenient system of numbering the [treatments] that are to be applied, will lighten the work of the man in the field, who is usually operating under averse conditions, is frequently in a hurry, and is sometimes not very certain of the points at issue." (F Yates, "The Design and Analysis of Factorial Experiments" Harpenden Imperial Bureau of Soil Science, 1937)
"The statistician who supposes that his main contribution to the planning of an experiment will involve statistical theory, finds repeatedly that he makes his most valuable contribution simply by persuading the investigator to explain why he wishes to do the experiment, by persuading him to justify the experimental treatments, and to explain why it is that the experiment, when completed, will assist him in his research." (Gertrude Cox, [lecture] 1951)
"What goes wrong [in long-range planning] is that sensible anticipation gets converted into foolish numbers: and their validity always hinges on large loose assumptions." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)
"A good rule of thumb for deciding how long the analysis of the data actually will take is (1) to add up all the time for everything you can think of - editing the data, checking for errors, calculating various statistics, thinking about the results, going back to the data to try out a new idea, and (2) then multiply the estimate obtained in this first step by five.
"Statistics is a tool. In experimental science you plan and carry out experiments, and then analyse and interpret the results. To do this you use statistical arguments and calculations. Like any other tool - an oscilloscope, for example, or a spectrometer, or even a humble spanner - you can use it delicately or clumsily, skillfully or ineptly. The more you know about it and understand how it works, the better you will be able to use it and the more useful it will be." (Roger Barlow, "Statistics: A Guide to the Use of Statistical Methods in the Physical Sciences", 1989)
"An important part of the explanation [of continued use of significance testing] is that researchers hold false beliefs about significance testing, beliefs that tell them that significance testing offers important benefits to researchers that it in fact does not. Three of these beliefs are particularly important. The first is the false belief that the significance level of a study indicates the probability of successful replications of the study [...]. A second false belief widely held by researchers is that statistical significance level provides an index of the importance or size of a difference or relation [...]. The third false belief held by many researchers is the most devastating of all to the research enterprise. This is the belief that if a difference or relation is not statistically significant, then it is zero, or at least so small that it can safely be considered to be zero. This is the belief that if the null hypothesis is not rejected then it is to be accepted. This is the belief that a major benefit from significance tests is that they tell us whether a difference or affect is real or ‘probably just occurred by chance’." (Frank L Schmidt, "Statistical significance testing and cumulative knowledge in psychology: implications for training of researchers", Psychological Methods 1(2), 1996)
"Consideration needs to be given to the most appropriate data to be collected. Often the temptation is to collect too much data and not give appropriate attention to the most important. Filing cabinets and computer files world-wide are filled with data that have been collected because they may be of interest to someone in future. Most is never of interest to anyone and if it is, its existence is unknown to those seeking the information, who will set out to collect the data again, probably in a trial better designed for the purpose. In general, it is best to collect only the data required to answer the questions posed, when setting up the trial, and plan another trial for other data in the future, if necessary." (P Portmann & H Ketata, "Statistical Methods for Plant Variety Evaluation", 1997)
"Meta-analytic thinking is the consideration of any result in relation to previous results on the same or similar questions, and awareness that combination with future results is likely to be valuable. Meta-analytic thinking is the application of estimation thinking to more than a single study. It prompts us to seek meta-analysis of previous related studies at the planning stage of research, then to report our results in a way that makes it easy to include them in future meta-analyses. Meta-analytic thinking is a type of estimation thinking, because it, too, focuses on estimates and uncertainty." (Geoff Cumming, "Understanding the New Statistics", 2012)
"Statistics can be defined as a collection of techniques used when planning a data collection, and when subsequently analyzing and presenting data." (Birger S Madsen, "Statistics for Non-Statisticians", 2016)
"The best time to plan an experiment is after you’ve done it." (Ronald A Fisher)
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