Importance of statistics in biotechnology
Tags: Statistics, Biotechnology

The practice or science of collecting and analyzing numerical data in large quantities, especially for the purpose of inferring proportions in a whole from those in a representative sample is called statistics. It is a branch of applied mathematics which deals with the collection, classification, analysis and interpretation of data. Statistics and experimental design are important tools for the biotechnologist and should be used when planning and conducting experiments as well as during the analysis and interpretation of results.

Statistics in biotechnology can be called biostatistics which deals with the study and methods of collection, presentation, analysis and interpretation of data of biological research. It is also called biometrics since it involves many measurements and calculations. In biostatistics, the statistical methods are applied to solve biological problems. Basic understanding of biostatistics is necessary for the study of biology particularly doing research in biological science. The statistics will help the biotechnologist to understand the nature of variability and deriving general laws from small samples.

Few applications of statistics in biotechnology are summarized below:

Research

· Research is incomplete without statistics

· Every data in the research need to be statistically validated.

· Statistics is needed to design the experiment .

· Deriving logical conclusions from the data.

· Deriving single values from a group of variables.

Establishing Sample Size

An important part of any biological experiment involves correctly choosing samples and selecting the right number of trials. A basic introduction to statistics provides background in statistical randomness and the law of large numbers. When conducting a study of whether insects prefer to eat American elm leaves or Princeton elm leaves, for example, using a properly randomized sample of both types of leaves helps control for confounding factors. For example, choosing a small number of leaves from the American elm, if those were all from a single tree, might result in a tree that was unusually filled with insects and would bias the results. Selecting many hundreds of leaves from a random sample of trees, however, reduces this type of error

Hypothesis Testing

When conducting experiments with a large sample, a biologist must make sure that a conclusion is statistically significant. One such experiment might involve examining whether smoking leads to cancer. By examining the means of two groups — one that smokes and one that doesn’t — a biologist might find that the smokers more commonly had cancer. Means, however, reflect the center of a distribution of data, and hypothesis testing involves examining the spread of that distribution. If the data is heavily clustered around the mean, the mean number is a reliable indicator; if the data is heavily spread out, the mean is less reflective of overall trends and should be considered in that context.

Interpreting Data Analyses

After finishing an experiment or observation, biologists need statistics to draw proper conclusions. For example, comparing the data from two groups of plants — one that was watered and one that was not — can lead to erroneous conclusions. A biologist, for example, might simply record the mean height of these two sets of plants and conclude that the watered plants grew taller. This, however, does not account for other statistical measures, such as variance. The nonwatered plants might have grown less tall on average, but perhaps their heights varied more widely than their watered counterparts, which is important data to relay in a conclusion.

Statistical Software

Very large data sets cannot easily be processed by hand. In many biological situations, such as those in ecology that use large sample sizes, using statistical software makes data processing more expedient. Data programs include Stata; Statistical Analysis System, or SAS; and Statistical Product and Service Solution, or SPSS. Most introductory statistics classes will use these software products, which can involve learning programming languages.

Genetics

· For studying the genetic structure of a population

· For mapping the chromosomes

· Essential for the study of Mandelian genetics

· To study the inheritance patterns of genes

· For studying the behavior of genes in a populations