Primacy of Statistics in Biological science

Statistical analysis provides essential insight into many biological processes, while biology focuses on living organisms. Basic statistical ideas assist biologists in effectively planning experiments, validating conclusions, and interpreting results. Many biology courses of study require a course in biostatistics that covers such concepts as randomized trials, hypothesis testing and the use of statistical software.
Choosing a Sample Size: Choosing the right samples and the appropriate number of trials is a key aspect of any biological experiment. The rule of large numbers and statistical volatility are extensively covered in a basic introduction to statistics. 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. Choosing a small number of leaves from an American elm tree, for example, assuming they were all from the same tree, could result in an extremely insect-infested tree, skewing the results. This type of mistake can be reduced by selecting hundreds of leaves from a random sample of trees.
Hypothesis Testing : A biologist must ensure that a result is statistically significant while conducting tests with a big sample. One such trial might look into whether smoking causes cancer. A biologist might determine that smokers are more likely to have cancer by comparing the means of two groups: one who smokes and one who does not.. 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 Analysis: 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.
Biologists Use Statistics : Other statistical comments can help in selecting the sample size or which organisms you have studied in the group. Although it may seem that selecting studies in a group will undoubtedly give you the best possible analysis group, random samples may accidentally produce samples that do not occur naturally without a sample group. Biologists are careful to use statistical techniques to help with samples to keep their results clean.
What types of statistical tests do biologists use? : The basic types of statistical tests used in biology fall into four basic categories: correlation, means of comparison, regression and heterogeneity. The correlation tests measure how two or more variables are involved. Compare means of measuring the difference between two or more variables or data sets. If a variable in a change is able to predict another variable and non-parametric tests are used, then the datasets do not meet the requirements of parametric analysis tests, and then they can be analyzed.
(The author is a Research Scholar at Statistics Department at SKUAST Jammu. Views are his own)
[email protected]