Preface – Biostatistics


Biostatistics: An Introduction has been designed to serve as a text for students studying science subjects such as biology, biotechnology and environmental science. In recent years, biostatistics has been used widely for solving research problems in life sciences. As with most tools, biostatistics is not of much use unless the user understands its application and purpose. In order to perform efficiently in the present complex world, a researcher in the life science field ought to know enough about the basic principles of data analysis and has to be certain that all available information is used effectively to solve a given problem. With this in mind, this text emphasizes statistical applications, statistical model building and finding the manual solution methods.

Target Audience

This book is intended to be used by beginners as well as advanced learners as a text in biostatistics for solving research problems in the field of applied statistics. The following groups of students stand to benefit from this book:

  • Graduate and postgraduate students of Biology, Botany, Environmental Science, and all other life science courses.
  • Students pursuing professional courses such as B.E. (Bioinformatics) and M.B.B.S.
  • Users of applied statistics, who need a comprehensive reference.


The text contains sufficient information for all courses. This allows teachers ample flexibility in adapting the text to their individual course plans. The text includes Introduction to statistics and its life science applications; Data structures; Data sources and data collection; Data representation; Measures of central tendency; Dispersion; Skewness, moments and kurtosis; Correlation and regression analysis; Probability, random variables and expectations; Discrete probability distributions and continuous probability distribution; Theory of sampling and Testing of hypothesis.


  • The problems discussed in the examples and in the exercises are related to the biostatistics papers of recently held university examinations.
  • This text is designed to accentuate the “self-taught” learning method.
  • For most of the methods, the required algorithm is clearly explained using flowcharts.

I hope that this text will meet the needs of those for whom it has been actually designed.