common errors in statistics
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AWARENESS PROGRAM ON STATISTICS
COMMON ERRORS IN STATISTICS
Dr. MURALIDHAR METTAM. V. Sc., PhD
Assistant ProfessorDepartment of Animal Genetics and Breeding
NTR College of Veterinary Science, GANNAVARAM
AWARENESS PROGRAM ON STATISTICS
Problems associated with statistical
analysis of biological data
• Misuse
• Misinterpretation
• Methodological limitations
AWARENESS PROGRAM ON STATISTICS
The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify." Darrell Huff (1954)
AWARENESS PROGRAM ON STATISTICS
"Almost every student of probability and statistics simply memorizes the rules. Most ... select their methods blindly, understanding little or nothing of the basis for choosing one method rather than another. This often leads to wildly inappropriate practices, and contributes to the damnation of statistics." Julian Simon and Peter Bruce (1999)
AWARENESS PROGRAM ON STATISTICS
"It is often easier to get a paper published if one uses erroneous statistical analysis than if one uses no statistical analysis at all." Stuart Hurlbert & Celia Lombardi (2003)
AWARENESS PROGRAM ON STATISTICS
The problem of poor statistical reporting is, in fact, longstanding, widespread, potentially serious, and not well known, despite the fact that most mistakes concern basic statistical concepts and can be easily avoided by following a few guidelines. Tom Lang (2004)
AWARENESS PROGRAM ON STATISTICS
It's science's dirtiest secret: The "scientific method" of testing hypotheses by statistical analysis stands on a flimsy foundation.... Even when performed correctly statistical tests are widely misunderstood and frequently misinterpreted." Siegfried, T. (2010)
AWARENESS PROGRAM ON STATISTICS
Confusing statistical significance
with clinical importance• Small differences between large groups
can be clinically meaninglesso Milk yield in large herds
• Large differences between small groups can be clinically important but not statistically significanto Treatment of cancer
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Not defining “normal” or “abnormal”
when reporting diagnostic test
results
AWARENESS PROGRAM ON STATISTICS
Not defining “normal” or “abnormal”
when reporting diagnostic test
results• The importance of statistical differences in
diagnostic test results depends on how a “normal” or abnormal value is defined
AWARENESS PROGRAM ON STATISTICS
Not defining “normal” or “abnormal”
when reporting diagnostic test
results• Diagnostic definition of normal:
o range of measurements over which the disease is absent and beyond which it is likely to be present
• Statistical definition: o measurements taken from a disease free population. o Assumes that the test results are normally
distributed. o Normal range is range of measurements includes 2
SD above and below the meano The highest and lowest 2.5% of values are abnormalo Not many test results are normally distributedo Eg: Serum-creatinine; Hb
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Assuming Correlation = Causation
• Sometimes correlations are over used• True in case of some studies publicized in
the media• Correlation does not imply causation
o Herd size vs body weight - if large herd tend to have higher body weights
o Herd size is causing increase in body weight?
AWARENESS PROGRAM ON STATISTICS
Misinterpreting Overlapping
Confidence Intervals• Standard deviation – spread of data• Standard error – accuracy of mean• Confidence interval (CI) – uncertainity associated
with sampling methods
AWARENESS PROGRAM ON STATISTICS
Misuse of standard deviation and
standard error• A related misuse of the standard error is to use it
as a descriptive statistic when it is in fact an inferential statistic
• Under normal distribution of the data standard deviation is the correct descriptive statistic to use as an indicator of variability between observations
• The standard error only reflects this variability for a particular sample size
• SD – observations• SE - Mean
AWARENESS PROGRAM ON STATISTICS
Interpreting studies with non-significant results and low statistical power as negative when they are in
fact inconclusive
AWARENESS PROGRAM ON STATISTICS
Interpreting studies with non-significant results and low statistical power as negative when they are in
fact inconclusive• The absence of proof is not proof of
absence• Statistical power is ability to detect a
difference of a given size• Several studies that report non-statistically
significant findings are under powered – hence they are inconclusive
AWARENESS PROGRAM ON STATISTICS
Not Distinguishing Between
Statistical Significance and Practical
Significance
AWARENESS PROGRAM ON STATISTICS
Not Distinguishing Between
Statistical Significance and Practical
Significance• It's important to remember that using statistics,
we can find a statistically significant difference that has no discernible effect in the "real world"
• Just because a difference exists doesn't make the difference important o Eg: pet foods package
AWARENESS PROGRAM ON STATISTICS
Not confirming that the data met the
assumptions of the statistical tests used
to analyze them
AWARENESS PROGRAM ON STATISTICS
Not confirming that the data met the
assumptions of the statistical tests used
to analyze them • There are hundreds of statistical tests, and
several may be appropriate for a given analysis • However, tests may not give accurate results if
their assumptions are not met • For this reason, both the name of the test and a
statement that its assumptions were met should be included in reporting every statistical analysis
AWARENESS PROGRAM ON STATISTICS
Not confirming that the data met the
assumptions of the statistical tests used
to analyze them • Some common problems are –
o Using parametric tests when the data are not normally distributed (skewed)
o Using tests for independent samples on paired samples, which require tests for paired data
AWARENESS PROGRAM ON STATISTICS
Not confirming that the data met the
assumptions of the statistical tests used
to analyze them
AWARENESS PROGRAM ON STATISTICS
For error free statistical analysis
• Set forth your objectives and the use you plan to make of your research before you conduct a laboratory experiment, a clinical trial, or survey and before you analyze an existing set of data.
• Define the population to which you will apply the results of your analysis
• List all possible sources of variation. Control them or measure them to avoid their being confounded with relationships among those items that are of primary interest
AWARENESS PROGRAM ON STATISTICS
• Formulate your hypothesis and all of the associated alternatives. List possible experimental findings along with the conclusions you would draw and the actions you would take if this or another result should prove to be the case. Do all of these things before you complete a single data collection form and before you turn on your computer
• Describe in detail how you intend to draw a representative sample from the population
For error free statistical analysis
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• Know the assumptions that underlie the tests you use. Use those tests that require the minimum of assumptions and are most powerful against the alternatives of interest
• Incorporate in your reports the complete details of how the sample was drawn and describe the population from which it was drawn. If data are missing or the sampling plan was not followed, explain why and list all differences between data that were present in the sample and data that were missing or excluded.
For error free statistical analysis
AWARENESS PROGRAM ON STATISTICS
To read…..
• Statistical Mistakes in research…http://influentialpoints.com/Training/statistical_mistakes_in_research_use_and_misuse_of_statistics_in_biology.htm
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