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ФTTBasics in Experimental Research
Dr AJIT SAHAI, M.Sc. PhD. (Stats.),
Editorial-Consultant -Biometrics, Indian Journal, of Urology (IJU)
Director-Professor of Biometrics, JIPMER, Pondicherry.
It is said that Nature is consistently playing in and we do have to surrender to the dynamic nature of this Universe. Continuous changes in Nature with change in time bring uncertainty and variability in each and every sphere of Science. Even well known and tested principles and laws do fail with test of time. This uncertainty and variability prevalent in nature makes it difficult to satisfy our inner-urge of acquiring knowledge. Encountering the uncertainty and variability and thereby understanding their role in rational explanation of the facts becomes the basic feature of sciences. We by no mean can control or over-power the factor of uncertainty but capable of measuring it in terms of probability. This measurement helps a lot in experimentation many ways and also in making out inferences with known/minimum interference of the chance or luck factor. It is true in the case of variability also, which once again cant be eliminated but easily measurable. The measures of deviation and central tendency play a key role in all research. Though this universe is full of uncertainty and variability, a large set of experimental / biological observations always tend towards a Normal distribution. This unique behavior of data is the key to entire inferential statistics. Population probability distributions such as; Normal, Binomial, Poisson and sampling distributions like Chi-square, Students t and F are frequently used for probability calculations and also for testing the hypotheses through various tests of significance.
Though still more difficult is to understand the role of Relativity hidden invariably in most of the scientific explanations, it is desirable for knowing the limitations and the care needed in acquiring even the scientific knowledge. Most of the qualitative characteristics involved in experimentation either as independent or dependent variables, are measured in relative terms. Defining absolute zero pain, stress or measuring health and disease or even quantitative variables like temperature where absolute zero is difficult to know, are the examples of inherent relativity in measurements and require special attention while making out inferences based on such measurements. We require extra intelligence to capture the play of relativity hidden in any experimentation or observation. Do we notice or feel that day and night we are riding a great spaceship called Earth, revolving around Sun with a speed of approximately 105,600 Kms. an hour or 30 Kms per second? But riding a motorbike with 100 Kms speed is exciting. The Sky looks blue but it is neither blue nor in existence? Objects get magnified looking with an eye of an elephant? By reasoning it can be seen that life and death co-exist and one owes its existence to other. The same is the case with pleasure and pain; good and bad; light and darkness; health and disease; rich and poor and so on. If either of the two are non existent then the other will perish.
Inductive and deductive reasoning: Repeating the experiments essentially under the same conditions and keenly observing the outcome each time and relating them to derive a fact is the system followed in inductive reasoning in science. Physics is an example of empirical science or inductive reasoning. Whereas Pure Mathematics is an example of formal science, or deductive reasoning where the conclusions are derived on the basis of existing facts, definitions, theorems, and axioms. If inductive reasoning helps us in developing the principles that can be generalized, the deductive reasoning guides us in generalized decision-making.
Error and Bias: No experimentation or observation can be totally free from errors and escape from bias. But we must identify and recognize them for their elimination as for as possible or to control and minimize the effect. Measurements even being valid, if lack in precision and accuracy, irrespective of the magnitude or quantity of deviation from the intended measurement, are called errors. One sided repeated errors or systematic errors are called bias. Selection or allocation biases, measurement bias, instrument bias, inter & intra investigator or observers bias, misclassification bias etc. are some of the frequently encountered bias. We know that the techniques of blinding, randomization, replication, standardization, selection of controls and to a great extent the experimental designs do help us to overcome some of them.
A variable takes on or can assume various values. But the same quantity may be a constant in some situation and a variable in another. The temperature for boiling water in plains is 100 degree C, but it may change at mountains or at Moon. The variables may broadly be classified in a number of ways such as, continuous & discrete, qualitative & quantitative, random & non-random etc. Various models use different terminologies to explain the role and status of variables. For example in epidemiology we use the terms independent, dependent and intervening variables; or parallel to that cause, effect and confounding / interacting variables; in certain situations the same are called input, process and output variables; in forecasting the nomenclature preferred is predicting, predicted and disturbing variables; in laboratory situations we pronounce them as experimental, outcome and chance / random variables and so on. A dependent or outcome variable can serve as an independent or input variable in another process. Researchers do experience hundreds of other terms used invariably to explain very specific role assigned to a variable in a particular situation, such as, pseudo variable, or dummy, proxy, nuisance, substitute, culprit, treatment, response, extraneous, manipulated and complex variables etc.
The clarity in knowing the variables of interest to be considered in a particular study helps a lot in recruitment of research tools, techniques and methods to be used during experimentation and use of statistical tests at the end of the study.
Measurement Scales: Each variable has its own limitation of measurement. A suitable nominal scale can just classify most of the qualitative variables. Whereas, only for a few of such characteristics it may be practical to put the classification in an ordered sequence by using an ordinal scale, still the distance between two points is not the same. But numerical or quantitative variables can always be measured either by interval scale or ratio scale. The interval scale is valid for certain interval of the possible measurement, as in case of temperature the freezing and boiling points of water have been the basis of scaling in absence of the knowledge of absolute zero temperature. However ratio scale has an absolute zero such as weight, height, and pulse rate etc. How sensitive we are at measurements or in making observation determines precision in inference. A neonate can hardly differentiate between the father and mother but an infant does. It is the wisdom and the sensitivity in measurement, which reveals that even the atoms of an element are all different if we consider the flow of electrons. If we go to that degree of sensitivity in measurement we find that even Nature is incapable of reproducing two exactly similar objects or subjects. If we were in a position to utilize the information, increasing the sensitivity in measurement would certainly be more revealing. But the approach to exhaust all resources for having Nano-sensitivity in measuring the input variables in contrast to Pico/micro-sensitivity used for measuring the output variables is wasting resources without any gain.
Experimental Designs: The purpose of an experimental design is to enhance the power of inference making by either eliminating undesired independent variables from the site of experiment or minimizing their effect during the experimentation, and also to allow the desired independent (or experimental) variables to their full exploitation for manipulations by the research investigator. Experimental designs also help in sequencing the deployment of experimental tools, techniques and methods. completely randomized and randomized block designs are a few examples. Clinical trials with or without randomization and blinding, self-controlled and without control or crossover designs are frequently used in clinical settings.
The Sample and Sampling: A study of entire population is impossible in most of the situations. Sometimes, the study process destroys (animal sacrifice) or depletes the item being studied. In such situations the only alternative is sample study. Otherwise also the sample results are often more accurate, apart from being quick and less expensive. If samples are properly selected, probability methods can be used to estimate the error in the resulting statistics. It is this aspect of sampling that permits investigators to make probability statements about the observations in a study. To summarize, bigger does not always mean better or more powerful in making inferences. For this reason, investigators must plan the sample size appropriate for their study prior to beginning research. This process called determining the power of a study. The sample size has to be directly proportional to the heterogeneity in the population, whereas, the sampling error is always inversely proportional to it. The techniques of sampling may be classified as Probability sampling such as; simple random sampling, Stratified, cluster, systematic, multi-stage and multi-phase sampling; and Non-Probability sampling such as; convenience sampling, inverse or quota sampling, judgment and purposive sampling etc. But non-probability sampling findings are usually not qualified for any generalizations as they lack to be representative of the entire population. It is not only the sample-size but also the sampling method equally responsible for the power of a study.
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