Calidad

CHAPTER 1

Introduction
1 INTRODUCTION Most analytical experiments produce measurement data which require to be presented, analysed, and interpreted in respect of the chemical phenomena being studied. For such data and related analysis to have validity, methods which can produce the interpretational information sought need to be utilised. Statistics provides such methods through the richdiversity of presentational and interpretational procedures available to aid scientists in their data collection and analysis so that information within the data can be turned into useful and meaningful scienti®c knowledge. Pioneering work on statistical concepts and principles began in the eighteenth century through Bayes, Bernoulli, Gauss, and Laplace. Individuals such as Francis Galton, KarlPearson, Ronald Fisher, Egon Pearson, and Jerzy Neyman continued the development in the ®rst half of the twentieth century. Development of many fundamental exploratory and inferential data analysis techniques stemmed from real biological problems such as Darwin's theory of evolution, Mendel's theory of genetic inheritance, and Fisher's work on agricultural experiments. In such problems, understanding andquanti®cation of the biological effects of intra- and inter-species variation was vital to interpretation of the ®ndings of the research. Statistical techniques are still developing mostly in relation to practical needs with the likes of arti®cial neural networks (ANN), fuzzy methods, and structure±activity relationships (SAR) ®nding favour in the chemical sciences. Statistics can be appliedwithin a wide range of disciplines to aid data collection and interpretation. Two quotations neatly summarise the role statistics can play as an integral part of chemical experimentation, in particular: `The science of Statistics may be de®ned as the study of chance
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2

Introduction

variations, and statistical methods are applicable whenever such variations affect the phenomena beingstudied.'1 `Statistics is a science concerned with the collection, classi®cation, and interpretation of quantitative data, and with the application of probability theory to the analysis and estimation of population parameters.'2 Both quotations highlight that statistics is a scienti®cally-based tool appropriate to all aspects of experimentation from planning through to data analysis to help understandthe data and to provide interpretations relevant to experimental objectives. Since all chemical measurements are subject to inherent variation, statistical methods provide a bene®cial tool for explaining the features within the data accounting for such inherent variation. Knowledge of statistical principles and methods (strengths as well as weaknesses) should therefore be part of the skills of anyscientist concerned with collecting and interpreting data and should also be an integral part of design planning. Statistics should not be considered as an afterthought only to be brought into play after data are collected, the `square peg into round hole' syndrome, which is how the application of statistical methods is often viewed within the scienti®c community. Applied chemical experimentationgenerally falls into one of three categories: monitoring, optimisation, and modelling. Monitoring is primarily concerned with process checking such as monitoring pollution levels, investigating how data are structured, quality assurance of analytical laboratories, and quality control of experimental material such as house reference materials (HRMs) and certi®ed reference materials (CRMs).Optimisation, often through exploratory or investigative studies, comes into play when wishing to optimise a chemical process which may in¯uenced by a number of inter-related factors. Instances where such experimentation may occur include optimisation of analytical procedures, optimisation of a new chemical process, and assessment of how different chemical factors cause changes to a chemical outcome....