Objective
Statistics and biostatistics are treated under a biological and practical issue, it is necessary to avoid the excess of theory and to make a practical approach supported by biological applications (including in the probability concept).
Statistics is a necessary tool for research, scientific publications, memories and theses.
It is to note that the purpose of this material is to know how to manipulate the statistical tests (chap. 9, 10, 11 and 12) and as a result to know how to apply them in biological problems.
Content
Chapter 1: Population and characters
 Definition of statistics and descriptive statistics
 Population and sample
 Statistical variables: qualitative variable, discrete variable, continuous variable, census and sampling.
Chapter 2: Frequency distribution
 Arrange the data in a table
 Frequency and relative frequency
 Most used Graphs : line diagram and histogram
 Cumulative frequency, cumulative relative frequency and corresponding graphs
 Cumulative distribution function and ascending cumulative curve.
Chapter 3: Characteristics parameters of a frequency distribution
 Mean
 Mode
 Median
 First and third quartile
 Variance et standard deviation
Chapter 4: Correlation and linear regression
 graphic illustration of possible link which can exist between two quantitative variables
 Definition et interpretation of the covariance
 Determination of the linear regression line by the method of ordinary mean square error.
 Definition and properties of linear correlation coefficient.
Chapter 5: Elements of combinatory analysis
 Definitions and properties of the arrangement, permutation and combination.
Chapter 6: Probability
 Definitions et properties of the probability of an event
 Conditional probability
 Independent event, mutually exclusive events.
 BAYES’s theorem.
Chapter 7: Usually distribution and numerical tables
 Discrete random variable and continuous random variable.
 Binomial distribution
 Poisson distribution
 Normal distribution
 Special continuous distributions
 The Use of the numerical tables
Chapter 8: Estimation
 The need to estimate
 Student distribution
 Point estimation of percentage, mean and variance
 Confidence interval of percentage, mean and variance.

Chapter 9: χ2 (chi square) test
 conformity Test of
 contingency table
Chapter 10: parametric tests
 The risk criteria in a practical example.
 Comparison of two independent samples (small and large sample)
 Comparison of two variances (Fisher’s test)
 Comparison of two paired samples (small and large sample)
 Comparison of observed mean to a value of reference (small and large sample)
 Comparison of two proportions
 Comparison of observed proportion to a reference one
 Pearson’s correlation test
 the pvalue criteria
 multiple regression
Chapter 11: Analysis of Variance (case of one factor – case of 2 factors without repetition)
 Comparison of many samples with a Normal distribution
Chapter 12: non parametric Tests
 The Mann Whitney ‘s Test for two independent samples
 The Wilcoxon ‘s Test for two paired samples
 The Kruskall Wallis’s Test for many independent samples
 Spearman’s rank correlation Test.