Statistics, Biostatistics, Frequency distribution
Introduction
Statistics is a branch of applied mathematics that deals with collecting, classifying, analysing, and interpreting data. The word statistics is derived from ‘status’ means a ‘political state’ or ‘government’.
Biostatistics is a branch of biological science that deals with the study and methods of collection, presentation, analysis and interpretation of data of biological research. Biostatistics is also called biometrics since it involves many measurements and calculations. In biostatistics, statistical methods are applied to solve biological problems. A basic understanding of biostatistics is necessary for the study of biology particularly doing research in biological science
The statistics will help the biologist to:
(1) Understand the nature of variability and
(2) Helps in deriving general laws from small samples
Francis Galton is called the ‘Father of Biostatistics’. He created the statistical concept ‘of correlation’. Sir Galton for the first time used statistical tools to study differences among the human population. He also invented the use of questionnaires and surveys for collecting data on human communities.
The term ‘Biometry’ was introduced by Walter Weldon
Categories of Statistics:
Statistics are classified into two categories:-
(1). Pure Statistics
(2). Applied Statistics
(1) Pure Statistics
Pure statistics is the basic statistics. Pure statistics is further classified into FOUR sub-categories.
(a). Descriptive statistics
(b). Analytical statistics
(c). Inductive statistics
(d). Inferential statistics
(a). Descriptive Statistics
➢ These are the statistical tools and analysis which describe and summarize the main features of the data.
➢ Example: Measure of central tendency (mean, median, mode), Measure of dispersion (range, standard deviation, mean deviation) etc.
➢ Descriptive statistics explains the characteristics of the data.
➢ They reduce the complexities of the data into simple and logical summaries.
(b). Analytical Statistics
Analytical statistics deals with all tools in the statistics used to compare different
variables.
➢ Analytical statistics helps to establish a functional relationship between variables (data).
➢ Example: Correlation and Regression
(c). Inductive statistics
➢ Inductive statistics is the use of statistical tools to generate conclusions on the basis of
random observations.
(d). Inferential statistics
➢ Inferential statistics is the application of statistical theories to analyze the research
problems.
➢ It Includes very complex calculations, analysis and comparisons.
➢ Example: Index numbers, statistical quality control, vital statistics etc
Classification of Biostatistics
Biostatistics is conventionally divided into two aspects:
(1). the design of experiments for getting or collecting the data.
(2). the statistical analysis or statistical method.
Steps in biostatistics
A biostatistician investigation is carried out through the following sequential steps.
(1). Collection of data (variable)
(2). Classification of the collected data
(3). Analysis of data
(4). Interpretation of data
Importance of Statistics in Biological Science
(1). Research
➢ Research is incomplete without the statistics
➢ Every result (data) in the research needs to be statistically validated.
➢ For the design of experiments
➢ Selecting the method of collection of data
➢ Deriving logical conclusions from the data
➢ Deriving single values from a group of variables
(2). Medical and Pharmaceutical Science:
➢ For checking the efficiency of drugs
➢ To find out the possible side effects of drugs
➢ For conduction of drug treatment trials
(3). Genetics:
➢ Study the inheritance patterns of genes
➢ Essential for the study of Mendelian genetics
➢ For the studying the genetic structure of a population
➢ Studying the behaviour of genes in a population (Population Genetics)
Limitations of Statistics
(1). Statistical laws are true on average. A single observation is not a statistic.
(2). Statistics cannot be applied to single/individual data.
(3). Statistical methods are best applicable to quantitative data.
(4). Statistical method cannot be applied to highly heterogeneous data.
(5). If sufficient care is not exercised in collecting, analyzing and interpreting the data, the statistical results might be misleading.
(6). Only a person expert in statistics can handle the statistical tools efficiently.
(7). There are too many methods to study a single problem in statistics
(8). Statistics do not depict the entire story or the phenomenon.
(9). Statistical results are not always beyond doubt.
(10). Some errors are possible in the statistical decisions. (Type I and Type II Errors in Statistics)
(11). We do not know whether an error has been committed or not.
