###
**Author:**
**Katsumi Kobayashi & K. Sadasivan Pillai**
**Published in:**
**CRC Press**
**Release Year:**
**2013**
**ISBN:**
**978-1-4665-1540-6**
**Pages:**
**226**
**Edition:**
**1st**
**File Size:**
**3 MB**
**File Type:**
**pdf**
**Language:**
**English**

Author: |
Katsumi Kobayashi & K. Sadasivan Pillai |

Published in: |
CRC Press |

Release Year: |
2013 |

ISBN: |
978-1-4665-1540-6 |

Pages: |
226 |

Edition: |
1st |

File Size: |
3 MB |

File Type: |
pdf |

Language: |
English |

###
__Description of ____A Handbook of Applied Statistics in Pharmacology__

__Description of__

Scientists involved in pharmacology have always felt that statistics is a difficult cult subject to tackle. Thus they heavily rely on statisticians to analyse their experimental data. No doubt, statisticians with some scientist c knowledge can analyse the data, but their interpretation of results often perplexes the scientists. Statistics play an important role in pharmacology and related subjects like, toxicology, and drug discovery and development. Improper statistical tool selection to analyze the data obtained from studies conducted in these subjects may result in erroneous interpretation of the performance- or safety- of drugs. There have been several incidents in pharmaceutical industries, where failure of drugs in clinical trials is attributed to improper statistical analysis of the quintessential. In pharmaceutical Research & Development settings, where a large number of new drug entities are subjected to high-throughput in vitro and in vivo studies, use of appropriate statistical tools is quintessential.

It is not prudent for the research scientists to totally depend on statisticians to interpret the understanding of their hard work. Factually, scientists with basic statistical knowledge and understanding of the underlying principles of statistical tools selected for analyzing the data have an advantage over others, who shy away from statistics. Underlying principle of a statistical tool does not mean that one should learn all complicated mathematical jargons. Here, the underlying principle means only ‘thinking logically’ or applying ‘common sense’. The authors of A Handbook of Applied Statistics in Pharmacology book, with decades of experience in contract research organizations and pharmaceutical industries, are fully cognizant of the extent of literacy in statistics that the research scientists working in pharmacology, toxicology, and drug discovery and development would be interested to learn. A Handbook of Applied Statistics in Pharmacology book is written with an objective to communicate statistical tools in simple language. Utmost care has been taken to avoid complicated mathematical equations, which the readers may find difficult to assimilate. The examples used in the book are similar to those that the scientists encounter regularly in their research. The authors have provided cognitive clues for selection of an appropriate statistical tool to analyse the data obtained from the studies and also how to interpret the result of the statistical analysis.

It is not prudent for the research scientists to totally depend on statisticians to interpret the understanding of their hard work. Factually, scientists with basic statistical knowledge and understanding of the underlying principles of statistical tools selected for analyzing the data have an advantage over others, who shy away from statistics. Underlying principle of a statistical tool does not mean that one should learn all complicated mathematical jargons. Here, the underlying principle means only ‘thinking logically’ or applying ‘common sense’. The authors of A Handbook of Applied Statistics in Pharmacology book, with decades of experience in contract research organizations and pharmaceutical industries, are fully cognizant of the extent of literacy in statistics that the research scientists working in pharmacology, toxicology, and drug discovery and development would be interested to learn. A Handbook of Applied Statistics in Pharmacology book is written with an objective to communicate statistical tools in simple language. Utmost care has been taken to avoid complicated mathematical equations, which the readers may find difficult to assimilate. The examples used in the book are similar to those that the scientists encounter regularly in their research. The authors have provided cognitive clues for selection of an appropriate statistical tool to analyse the data obtained from the studies and also how to interpret the result of the statistical analysis.

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