basics in experimental research
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Basics in Experimental Research. Dr. AJIT SAHAI Director – Professor Biometrics JIPMER, Pondicherry. Dynamic nature of this U n i v e r s e. this very continuous change in Nature brings - uncertainty and - variability in each and every sphere of the Universe. - PowerPoint PPT PresentationTRANSCRIPT
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Basics in Experimental Research
Dr. AJIT SAHAI Director – Professor Biometrics
JIPMER, Pondicherry
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•Dynamic nature of this
U n i v e r s e
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this very continuous change in Nature brings - uncertainty
and - variability
in each and every sphere of the Universe
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This
uncertainty and variability
prevalent in nature makes it difficult to satisfy our inner-urge of
-acquiring knowledge
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Encountering the uncertainty and variability
- thereby understanding their role in
- rational explanation of the facts becomes the basic feature of sciences
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We by no mean can
control or over-power
the factor of uncertainty but capable of measuring it in terms of
Probability
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This measurement helps a lot
- in experimentation and
- making out inferences
with minimum interference of the chance or luck factor
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. It is true in the case of
variability also,which once again can’t be eliminated but easily measurable.
The measures of deviation & central tendency play a key role in all research.
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Biological Observations
Though this universe is full of uncertainty and variability,
a large set of experimental / biological observations always tend towards a
Normal distribution.
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Inferential Statistics
This unique behavior of data is the key to entire inferential statistics.
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Population Probability Distributions
such as;
Normal Binomial
PoissonRectangular
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Sampling Distributions
like
Chi-square, Student’s ‘ t’
and ‘ F’
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Mean
95.5%
99.7%
The role of Central tendency
and Deviation
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Population & Sampling Distributions
frequently used for probability calculations and also for
testing the hypotheses through various tests of significance
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Relativity
Understanding
the Relativity Component
hidden invariably in most of the scientific explanations is still more important
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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
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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.
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Inductive 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
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Deductive Reasoning:
‘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.
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The Principles and decision-making
If inductive reasoning helps us in developing the principles that can be generalized,
the deductive reasoning guides us in generalized decision-making.
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Measurement Scales
Nominal scale
Ordinal scale
Interval scale
Ratio scale
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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
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-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.
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- Selection or allocation biases, - Measurement bias,- Instrument bias, - Inter & intra investigator or - Observer’s bias, - Misclassification bias etc. are some of the frequently encountered
bias
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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.
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variable
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
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Classification
The variables may broadly be classified in a number of ways such as,
continuous & discrete, qualitative & quantitative, random & non-random etc.
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terminologies and role of variables
Various models use different terminologies to explain the role and status of variables
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terminologies and role 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’;
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terminologies and role of 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.
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Changing role of Variables
A dependent or outcome variable can serve as an independent or input variable in another process
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Changing role of Variables
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
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Clarity in knowing the variables
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.
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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
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Experimental Designs
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.
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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.
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Advantages
sample results are often more accurate, apart from being
quick and less expensive
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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
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Sample size and sampling error
The sample size has to be directly proportional to the heterogeneity in the population,
whereas, the sampling error is always inversely proportional to it.
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Probability sampling
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
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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.
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Power of a study
It is not only the sample-sizebut also the sampling method
equally responsible for the power of a study.
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To summarize
bigger does not always mean better or
more powerful in making inferences.
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For this reason, investigators must plan the sample size appropriate for their study prior to beginning research