Course Contents

Introduction and scope, use of statistics in biology. Population and sample. Stages of research, types of data and methods of data collection. Data arrangement and presentation, formation of tables and charts. Measures of central tendency computation of mean, media and mode from grouped and ungrouped data. Measures of dispersion, computation of variance, standard deviation, standard error and their coefficients. Probability rules. Binomial, poison and normal distributions. Hypothesis testing, Student ‘t’ test, Chi square test, Analysis of variance and LSD. Correlation and regression. Experimental designing, planning of an experiment, replication and randomization. Learning how to use Sigmastat on different types of data.

Course Synopsis

Upon completion of the course, students are able to: Recognize and give examples of different types of data arising in public health and clinical studies Calculate and interpret confidence intervals for population means and proportions Interpret and explain a p-value Perform a two-sample t-test and interpret the results; calculate a 95% confidence interval for the difference in population means Select an appropriate test for comparing two populations on a continuous measure, when the two sample t-test is not appropriate Understand and interpret results from Analysis of Variance (ANOVA), a technique used to compare means amongst more than two independent populations Choose an appropriate method for comparing proportions between two groups; construct a 95% confidence interval for the difference in population proportions Use SPSS Compare proportions amongst two independent populations

Course Learning Outcomes

It will help the students to analyze data pertaining to their research work and to assess the significance of their experimental designs. Without statistical analysis research articles are not accepted for publication by the scientific journals. So students must have sound knowledge of the statistical programs.