1) Missing specifications and hidden assumptions in Medical Statistical hypotheses tests.
In this paper, we analyse the implicit hypotheses used in Medical Statistical Hypotheses Tests, that are not usually experimentally tested and we bring them to the surface. We suggest new formulations and specifications for many familiar tests.We speculate on the development of methods that could be classified as nonparametric forecasting, nonparametric regression, and nonparametric disciminant analysis. The next table gives some examples:
Name of The test 
Used for 
Distribution used 
Assumptions not tested, 
Additional test required 
Vague specifications 
New formulae or specifications required witch is
not the size of the sample for the power or significance level 
Ztest, variance known 
Comparison of means 
Normal 
Normal Population 
Test for the Normality of
Population Test for the equality of variance in the two samples version 
"Large sample" 
Minimum size of the sample
according to the accuracy level of normality 
Ztest, Variance unknown 
Comparison of means 
Normal 
Normal Population 
Test for theNormality of Population Test for the equality of variance in the two samples version 
"Small Sample" 
Minimum size of the sample
according to the accuracy level of normality 
Ttests 
Comparison of means 
Student 
Normal Population 
Test for the Normality of Population Test for the equality of variance if it is assumed in the two samples version 
"Small Sample" 
Minimum size of the sample
according to the accuracy level of normality 
Yate's corrected X^2 independence test 
Comparison of proportions 
X^2 
Normal Approximation 

"Sufficient large sample to apply normal approximation" 
Minimum size of the sample
so as to have no error up to the accuracy level of normality approximation 
Signed rank (Wilcoxon) Test 
Comparison of medians 
Nonparametric 


"Sufficient Large sample to make use of the normal approximation in the central limit theorem" "Small sample", "Large sample" 
Minimum size of the sample
according to the accuracy level for the central limit theorem to apply
without error 
McNeamars Test 
Comparison of proportions 
X^2 
Normal Approximation 

"Sufficient large sample to apply normal approximation" 
Minimum size of the sample
according to the accuracy level of normality approximation 
Woolf's test 
Comparison of proportions 
X^2 
Normal Approximation 

"Sufficient large sample to apply normal approximation" 
Minimum size of the sample
according to the accuracy level of normality approximation 
Logrank test 
Comparison of proportions 
X^2 
Normal Approximation 

"Sufficient large sample to apply normal approximation" 
Minimum size of the sample
according to the accuracy level of normality approximation 
Signtest 
Comparison of proportions 
Nonparametric 

Test for the equality of
distributions in the two samples version 
"Sufficient Large sample to make use of the normal approximation in the central limit theorem" "Small sample", "Large sample" 
Minimum size of the sample
according to the accuracy level for the central limit theorem to apply
without error 
Anova's Ftest 
Comparison of means 
F 
Normal Population, equality of variances 
Test for the Normality of the Population. Test fo the equality of
variances 

Minimum size of the sample according to the accuracy level of normality 






