Abstract   
The term "statistics" has always been associated with studies relating to facts and figures and has been defined as a discipline concerned with the treatment of numerical data. The purpose of this article is to emphasize the importance of statistics in collection, presentation, analysis and interpretation of comparative data, as any research outcome depends to a large extent on the analysis of the comparative values obtained. This paper outlines the steps involved in a statistical study, namely selection of sample size, selection of type of test to be employed and analysis of results in a simplified manner
How to cite this article: Gupta T, Ballal S, Bharaadwaj N, Kandaswamy. Vital Statistics. J Conserv Dent 2005;8:124 
Introduction   
The final outcome of an experiment or research depends to a large extent on the analysis of comparative values obtained. To the dictum of Helmholtz^{ [8]} that "All science is measurement", one can also add Sir Henry Dale's clause that, "All true measurement is essentially comparative". Statistics plays an integral role in collection, presentation, analysis and interpretation of comparative data. In a broad sense, the term "statistics" has always been associated with studies related to facts and figures e.g. health statistics, business statistics etc. In the book "Statistical methods in medical research" statistics has been defined as a discipline concerned with the treatment of numerical data derived from groups of individuals or materials^{ [8].} STEPS IN STATISTICAL STUDY
The chronology of steps involved in a statistical study are as follows:
SELECTION OF SAMPLE SIZE
Often, the primary problem encountered by a student of research is the number of samples or sample size to be selected.
Crieteria for selection of sample size are as below:
 A sample size of 2530 in each group is adequate if there is one variable or one parameter in the study.
 In invivo studies where there is less availability of samples, a slight decrease in sample size may be acceptable.
 LARGER SAMPLE SIZE WILL BE NEEDED IF:
 Larger variation is expected
 Rare characteristic is present
 More variables are present
 More precision required
 More reliability required
SELECTION OF TEST
The tests employed to complete a study can be classified as:
A. For comparison of mean (average of observations) of different samples.
B. For comparison of proportion (percentage) of different samples.
C. Correlation tests
D. Regression tests.
A. For comparison of mean (average of observations) of different samples
Two types of tests are available, namely parametric and nonparametric
Parametric tests
 Employed if the distribution of the population from which the samples are drawn is known'. (i.e. normally distributed with less variation).
 In the computation of parametric tests the arithmetic processes of addition, division and multiplication are used^{ [6]} .
 Used if adequate sample size is present^{ [6]} .
(i) Independent ttest
Employed to compare mean of two groups using one variable^{ [2]} . Eg Comparison of bond strength of amalgam and composite.
(ii) Paired ttest
Used for within group comparisons
at different time intervals^{ [1]} . Eg. Number of microbes in root canal before and after antibiotic therapy.
(iii) ANOVA (Analysis of variance)
Mean of any number of groups using one variable is determined by this test^{ [5]} . Eg. Sealing capacity of different endodontic sealers.
Non parametric tests
Employed if distribution is unknown^{ [6]} .(large variation present)
Data are changed from measurements or scores to ranks or even to signs^{ [6]} .
Used if adequate sample size is not present^{ [6]} .
(i) Mann Whitney U test
It is equivalent to independent ttest^{ [3]} .
(ii) Wiloxan sign rank test It is equivalent to paired ttest.
(iii) Kruskal Wallis Htest
It is equivalent to ANOVA.
B. For comparison of proportion (percentage) of different samples
The test employed are as follows:
(i) Chisquare test
It checks the proportion between any number of groups using one variable^{ [7]} . It is used if adequate sample size is available.
Eg. The effect of ampicillin, sulphonamides and tetracycline in a certain percentage of people.
(ii) Fisher's test
This test is. similar to Chisquare test if less sample size is available.
(iii) McNemar test
It compares proportion of one variable within a group at different time intervals.
Eg. Proportion of people having sensitivity before and after using desensitizing paste.
C. Correlation tests
It is used to find if two variables covary with each other or are independent.
Eg. The susceptibility rate of organisms in root canal following increase in antibiotic dosage.
D. Regression tests
It describes the dependence of one variable on another independent variable.
E_{ L} . Effect of bonding agent on strength of composite.
ANALYSIS OF RESULT
 Nonparametric tests like Mann Whitney U test, Wiloxan sign rank test, Kruskal Wallis H test are less sensitive than parametric tests like Independent ttest, ANOVA as they use random ranking instead of original values.
 Probability (p) value indicates level of significance (sensitivity) of a test^{ [4]} .
p < 0.001> highly significant
The probability that the difference between two groups occurring by chance is less than I in 1000.
p< 0.01 > Moderately significant
The probability of the difference occurring by chance is less than 1 in 100.
p < 0.05> Less significant
The probability of the difference occurring by chance is less than 5 in 100.
p> 0.05> Not significant
The probability of difference occurring by chance is very high.
Conclusion   
This paper attempts at explaining importance of statistics as an essential protocol for any research program. Statistics is the greatest leveler. It covers up for all the variations that can creep into the results thereby providing a foolproof system for proper interpretation of data. Hence it would be appropriate to term it as "The Vital Statistics".
References   
1.  Douglas G. Altman : Practical statistics for medical research, 3^{ rd} Edition, Chapman and Hall, 1991 : 191. 
2.  Leslie E. Dale, Geoffrey J.Bourke : Interpretation and Uses of Medical Statistics, 5^{ th} Edition, Blackwell Science Limited, 2000:207. 
3.  Martin Bland : An Introduction to Medical Statistics, 3^{ rd} Edition, Oxford University Press. 2000: 211. 
4.  Michael J. Campbell, David Machin : Medical Statistics  A Commonsense Approach, 3^{ rd} Edition, John Wiley and Sons Limited, 1999 8182. 
5.  P. Armitage, G. Berry : Statistical Methods in Medical Research, 2^{ nd} Edition, Blackwell Scientific Publication, 1987 : 187. 
6.  P.S.S. Sundar Rao, J.Richard : An introduction to Biostatistics. 3^{ rd} Edition, Prentice Hall of India Private Limited. 2003 : 107109. 
7.  RA Brown, J Swanson Beck : Medical Statistics on Personal Computers, 2^{ nd} Edition, BMJ Publishing Group, 1994 : 7273. 
8.  Sir Austin Bradford Hill : A Short Textbook of Medical Statistics, 10^{ th} Edition, ELBS and Hodder and Stoughton, 1977 : 28. 
Correspondence Address: Tina Gupta Meenakshi Ammal Dental College and Hospital, Chennai India
Source of Support: None, Conflict of Interest: None  Check 
DOI: 10.4103/09720707.42599
