By David J. Livingstone
Encouraged through the author's desire for sensible assistance within the methods of information research, a realistic consultant to medical facts research has been written as a statistical significant other for the operating scientist. This guide of information research with labored examples makes a speciality of the appliance of mathematical and statistical innovations and the translation in their results.Covering the most typical statistical equipment for studying and exploring relationships in info, the textual content contains large examples from a number of medical disciplines.The chapters are organised logically, from making plans an scan, via reading and showing the information, to developing quantitative types. every one bankruptcy is meant to face on my own in order that informal clients can confer with the part that's excellent to their problem.Written by means of a hugely certified and the world over revered writer this text:Presents statistics for the non-statisticianExplains a number of ways to extract info from dataDescribes the appliance of statistical easy methods to the layout of “performance chemicals”Emphasises the applying of statistical ideas and the translation in their resultsOf useful use to chemists, biochemists, pharmacists, biologists and researchers from many different medical disciplines in either and academia.
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Additional info for A Practical Guide to Scientific Data Analysis
As described in the last chapter, any experimental result, whether a measurement or the yield from a synthesis, comes from a population of such results. When we do an experiment we wish to know about the population structure (values) using a sample to give some idea of population behaviour. In general, the larger the number of samples obtained, the better our idea of population values. The advantages of well-designed experiments are that the information can be obtained with minimum sample sizes and that the results can be interpreted to give the population information required.
If we had measurements for all possible values of a variable (an infinite number of measurements), then we would be able to compute a value for the population distribution. Statistics is concerned with a much smaller set of measurements which forms a sample of that population and for which we can calculate a sample distribution. A well-known example of this is the Gaussian or normal distribution. One of the assumptions made in statistics is that a sample REFERENCES 23 distribution, which we can measure, will behave like a population distribution which we cannot.
Is there anything that can be done to improve a poorly distributed variable? The answer is yes, but it is a qualified yes since the use of too many ‘tricks’ to improve distribution may introduce other distortions which will obscure useful patterns in the data. The first step in improving distribution is to identify outliers and then, if possible, identify the cause(s) of such outliers. If an outlier cannot be ‘fixed’ it may need to be removed from the data set. The second step involves the consideration of the rest of the values in the set.