Experimental design and data analysis for biologists pdf download

G Phone: in Conrinenral U. All other loc1tiom: , FAX: 5! Defining the Experimental Program Like many ideas, the decision to turn the course into a book came about after a little too much wine with dinner-a dinner with fellow scientists Dan Finley, Fred Goldberg, and Allan Weissman-at which we were discussing both the odd fact that experimental design was not commonly taught to prospective biologists in graduate school, and the obvious discontinuities between the demands of Critical Rationalism as written and the way that science was actually practiced.

Of course, this book still would not have been produced were it not accepted by a publisher. Sian Curtis then edited the manuscript in an extremely able fashion, with the help of Ginger Peschke and Maria Smit. Thanks very much to Sian and Jan tor their steady feedback and enthusiasm. Thanks so much also to Rena Steuer for expert production guidance, to Susan Schaefer tor typesetting, and to Denise Weiss for her design expertise.

A great deal of help in writing this was lent by Kumar Dharmarajan. Kumar is a former student and intern in my laboratory, who was a medical student at Columbia when much of the book was written.

He was thus able to rake on the role of rhe "prospective audience" and gave invaluable feedback on each chapter, identifYing passages rhat were unclear and asking questions rhat helped in the rewriting. Thanks very much to him fur spending so much time on this project. The authors explain how to use R and Bioconductor for the analysis of experimental data in the field of molecular biology.

The content is based upon two university courses for bioinformatics and experimental biology students Biological Data Analysis with R and High-throughput Data Analysis with R.

The material is divided into chapters based upon the experimental methods used in the laboratories. One great benefit of R and Bioconductor is that there is a vast user community and very active discussion in place, in addition to the practice of sharing codes.

Further, R is the platform for implementing new analysis approaches, therefore novel methods are available early for R users. Readers are provided with a detailed introduction and orientation to statistical analysis as well as practical examples to ensure a thorough understanding of the concepts and methodology. Specific emphasis is on the practical application of statistics in the biological and life sciences, while enhancing reader skills in identifying the research questions and testable hypotheses, determining the appropriate experimental methodology and statistical analyses, processing data, and reporting the research outcomes.

The book is also appropriate as a reference for researchers and professionals in the fields of anthropology, sports research, sports science, and physical education. The author of numerous journal articles and a member of the American Statistical Association, she received her PhD in Anthropology from the University of Tennessee.

Experimental Design for the Life Sciences explains how to organise experiments and collect data to make analysis easier, and conclusions more robust. An approachable and articulate style conveys even the most challenging concepts in clear and practical terms, showing how experimental design is about clear thinking and biological understanding, not mathematical or statistical complexity.

A straightforward introduction to a wide range of statistical methods for field biologists, using thoroughly explained R code. Even though an understanding of experimental design and statistics is central to modern biology, undergraduate and graduate students studying biological subjects often lack confidence in their numerical abilities. Allaying the anxieties of students, Introduction to Statistics for Biology, Third Edition provides a painless introduction to the subjec.

Experimental Design and Data Analysis for Biologists. Author : Jerry P. Queen,Gerry P. Some features of the site may not work correctly. DOI: Quinn and Michael J. Quinn , M. Keough Published 8 April Mathematics 1. Introduction 2. Estimation 3. Hypothesis testing 4. Graphical exploration of data 5.

Correlation and regression 6. Multiple regression and correlation 7. Design and power analysis 8. Comparing groups or treatments - analysis of variance 9.

Multifactor analysis of variance Randomized blocks and simple repeated measures: unreplicated two-factor designs Examples are researches carried out on human beings and survey studies. These types of researches are widely used in optimization of industrial production processes by trying different levels of the factors input variables so as to improve the quality and quantity of the output variables. An experiment is therefore, a test or a series of tests in which purposeful changes are made to the input variables of a process or system so that we may observe and identify the reasons for changes that may be observed in the output responses.

Sample is part of the population and Sampling is a systematic selection of some representative part of it so that the inference made later about the result of the study will work for the entire population. Sampling is important because a study cannot be feasible on the entire population due to time and budget constraints.

Sampling should be carried out free of bias by using randomization principles. Selection of samples randomly from the population will avoid systematic bias and help in precision small random error of the result. Data is a fact that we record by observing the sample. Data collected is statistically analyzed or processed and the information extracted will be inferred to the entire population from which the samples were taken.

Data a researcher collects can be either qualitative or quantitative. Examples can be a color orange, gray, green ; Taste bitter, sour, salty, sweet ; aroma spicy, flowery, fruity of a food sample. Examples can be moisture content of a food product, height of a green bean, drying temperature, concentration of a solution. Quantitative data can further be categorized into various classes.

Examples can be a number of human beings brothers, sisters, students in a class , number of animals hens, sheep , and number of cars in a park. Examples include height, mass, length etc. Data collected in social science researches can also be categorized into four each one adding to the other. The four categories are ratio, interval, ordinal and nominal. Accuracy refers the closeness of the data to the actual value and that is preferable.

We would also prefer that if we were to repeat our data collection procedure the repeated values would be as close to each other as possible and this is referred to as precision. Another way to describe these ideas is to say that a measurement has high accuracy if it contains relatively small systematic variation.

It has high precision if it contains relatively small random variation. Precision will lead to accuracy unless there is a bias in the way we do a measurement. For example, a balance could be precise but mis- calibrated. In that case, we would get weights that are repeatable precise , but inaccurate. On the other hand, the balance could be imprecise in determining weights. In this case occasionally the balance would provide weights that are accurate, but it will not do so reliably, for at the next measurement the weight will be different.

Without precision we therefore cannot obtain accuracy. Precision has to do with the quality and resolution of the devices or methods with which we measure variables; accuracy, with how we calibrate the devices or methods once we have obtained precision.

Engineering research is the systematic process of learning about and building new technologies for the purpose of designing a product. As opposed to scientific research, engineering research is not concerned with discovering how the world works, but rather how things can be made to function for a given purpose.

Such research might involve much scientific study, however, as engineers work to create design solutions to real- world problems. In the context of manufacturing for example, inputs are factors or process variables such as people, materials, methods, environment, machines, procedures, etc.

In performing a designed experiment, we will intentionally make changes to the input process or machine variables or factors in order observe corresponding changes in the output process. The factors experimenter changes are called independent variables and those the experimenter observes or measures are called dependent variables. The information gained from properly planned, executed and analyzed experiments can be used to improve functional performance of products, to reduce scrap rate or rework rate, to reduce product development cycle time, to reduce excessive variability in production processes, and so on.

These can also be further classified as i. Design factors — are those actually selected for study in the experiment ii. Held-constant factors — are variables that may exert some effect on the response, but for purposes of the present experiment these factors are not of interest, so they will be held at a specific level. Allowed to vary factors — are factors that may influence the responses but only to limited extent and their effect is averaged out by randomization.

Example can be variability in samples. Nuisance factors are often classified as: i. Controllable factors — a controllable nuisance factor is one whose factor may be set by the experimenter. Examples can be different batches of raw materials, different days of the week.

Blocking principle discussed in section 2. Uncontrollable factors — these are nuisance factors that cannot be controlled by the experimenter, but its effect can be measured and compensated by an analysis procedure called analysis of covariance. Example if the relative humidity of a process environment cannot be controlled it can be measured and treated as a covariate. Noise factors — are those factors varying naturally and uncontrollably in the process, but can be controlled for purposes of an experiment.

The objective usually to find settings of the controllable design factors that minimize the variability transmitted from the noise factors. Figure 1 below shows general model of a process.

In order to draw statistically sound conclusions from the experiment, it is necessary to integrate simple and powerful statistical methods into the experimental design methodology. There are three basic principles of experimental design: Randomization, Replication and blocking which improve the efficiency of experimentation. These principles of experimental design are applied to reduce or even remove experimental bias.

Large experimental bias can result in wrong optimal settings or in some cases it could mask the effect of the really significant factors. The details of the three principles of DOE are discussed next.

Such factors can adversely affect the experimental results and therefore, must be either minimized or removed from the experiment. Randomization is one of the methods experimenters often rely on to reduce the effect of experimental bias. By randomization we mean that both the allocation of the experimental material and the order in which the individual runs or trials of the experiment are to be performed are randomly determined.

Replication also helps the experimenter to obtain a more precise estimate of a factor when sample mean is to be used to estimate the effect of this factor. Often blocking is used to reduce or eliminate the variability transmitted from nuisance factors factors that may influence the experimental responses but in which we are not directly interested.

Guidelines for designing an experiment: 1. Recognition of and statement of the problem 2. Choice of factors, their levels and ranges Pre-experimental planning 3. Selection of the response variables 4. Choice of experimental design 5. Performing the experiment 6. Statistical analysis 7. Conclusions and recommendations 2. In the context of DOE, the number of degrees of freedom associated with a process variable is equal to one less than the number of levels for that factor.

Suppose an experiment is performed in eight trial experiment and each trial condition was replicated twice. The degrees of freedom for an interaction are equal to the product of the degrees of freedom associated with each factor involved in the interaction.

In other words, one cannot estimate factor effects and their interaction effects independently. Effects which are confounded are called aliases. A list of confounding which occur in an experimental design is called an alias structure or a confounding pattern. One statistic for indicating the center of this data set is the sample mean, defined to equal the arithmetic average of the data values.

Any extreme values extremely low or extremely high values affect the average in such away it does not represent the center of the data set. B Sample Median A statistic that is also used to indicate the center of a data set but that is not affected by extreme values is the sample median, defined as the middle value when the data are ranked in order from smallest to largest.

We will let m denote the sample median. Definition Order the data values from smallest to largest. If the number of data values is odd, then the sample median is the middle value in the ordered list; if it is even, then the sample median is the average of the two middle values.

It follows from this definition that if there are three data values, then the sample median is the second-smallest value; and if there are four, then it is the average of the second- and the third-smallest values.

C Sample Mode Another indicator of central tendency is the sample mode, which is the data value that occurs most frequently in the data set. Exercises 1. Compute mean, median and mode for the following data sets.

Remember me on this computer. Enter the email address you signed up with and we'll email you a reset link. Need an account? Click here to sign up. Download Free PDF. Quinn and Michael J. Peter Petraitis. A short summary of this paper.