Biostatistics

Bi·o·sta·tis·tics : The branch of statistics that deals with data relating to living organisms. Using mathematical and statistical tools in biological sciences

‘’Making sense of all the data’’; “Turning data into knowledge.”; ‘’Convert numbers to ideas’’

       Topics covered include populations and samples, variables, probability distributions, descriptive statistics, statistical inference, and hypothesis testing. Included are selected parametric and non-parametric tests for examining differences in means, variances, and frequencies as well as correlation, regression, and tests of independence. Emphasis is given to practical matters such as how to choose appropriate analyses and how to interpret results, both statistically and biologically. High school algebra is the only math background you need. Biostats is a practical application course - to learn it, you have to do it. Failing to apply statistical concepts and procedures on a regular basis will diminish your chances of understanding the material and earning the grade you desire.
Student Learning Outcomes – By the end of the semester you will be able to:  understand how science and statistics interact  apply basic statistical procedures using professional statistical software  read and understand primary biological literature

Subject matter content

1. Definitions and statistical basics  

2.1. Collection of data and sampling

2.2. Population

2.3. Sample

2.4. Randomness

2.5. Variables  

2. Descriptive statistics

1. Data representation and ploting  

1.1. Quantitative variable

1.2. Qualitative variable

1.3. Frequency distributions

1.4. Graphs

2. Description data (data reduction)

2.1. Measures of central tendency

2.2. Measures of dispersion

3. Inferential Statistics

1.1. The confidence interval of mean

1.2. Tests of conformity

1.3. Confidence intervals and tests of significance for the difference between two means : Independent sample

1.4. Confidence intervals and tests of significance for the difference between two means: Dependent sample

2. Analysis of variance (ANOVA)

3. Normality test (Shapiro-Wilk)

4. Comparing variance

5. Comparing frequency, proportion and percentage (Chi-square test χ 2)

6. Correlation and regression   

4. Non parametric statistics

1.  Mood's median test

2. Wilcoxon test

3. Kruskal –Wallis test

4. Friedman test

5. Practical analysis of data