Biological Statistics 1

INTRODUCTION

The subject of Biological statistics deals with the application of statistical knowledge in the study of Biology. Both Biology and Statistics are part of the branches of science. While the former deals with the study of living organisms, Statistics deals with the collection, analysis, and interpretation of numerical data with the sole purpose of arriving at an inference.

The study of living organisms most often requires that research are performed and data collected through the various field and or laboratory experiments. The data collected serves no real purpose until a pattern can be formed through analysis. Hence, the data must be subjected to statistical analysis. Biological statistics, therefore, can be thought of as applied statistics.

RESEARCH AND DATA COLLECTION

Data are generally defined as the individual observation or measurement that are made on the smallest sample unit or any set of recorded observation on the population of interest. There is no data without research. The primary purpose of conducting research is to collect data and the analysis of these data will enable researchers to arrive at a conclusion and achieve the aim and objectives of their research. Research is meant to address problems or fill knowledge gaps that exist in the understanding of natural phenomena – the study of organisms in this case. As in every science, conducting research requires that the scientific method is followed. The scientific method is a series of chronological steps that must be observed in order to arrive at a relevant conclusion about natural phenomena. These steps are:

1. Problem identification: the problem to be solved by the research must have been identified before research can be conducted. Problems are identified through observations.
2. Aim and objectives: after the problem to be solved has been identified, next is to establish the main aim of the research and the objectives that will assist in achieving this aim.
3. Decision on data collection: the data to be collected must be the one that is relevant to the objectives of the research. For example, in research whose aim is to find the average length of flagella in a particular bacterium, the data to be collected will be the length of the flagellum in each bacterium of study. Collecting data on the size of the cell would be irrelevant to the research. Also, the collected data must be sufficiently large and be representative of the entire population of the study. Before collecting the data, however, a decision must be made on the type of statistical analysis that they will be subjected to in order to achieve the set objectives of the research.
4. Collection of data: data are collected through a suitable experimental design. Most of the time, the entire population of interest cannot be studied and as such, a representative of the population (sample) is selected to represent the entire population. This is known as sampling. There are different methods of selecting samples for research. The major ones are:

• Random sampling
• Stratified sampling
• Systematic sampling
• Stratified random sampling
• Semi-systematic sampling
• Clustered sampling

1. Statistical analysis and data interpretation
   After data collection, the next thing is to subject the data to relevant statistical analysis. There are two types of statistical analysis. These are

• Descriptive statistical analysis: this deals with the arrangement of the collection that such a way that trends can be seen at a glance. It involves arranging data into tables, charts, graphs, pictograms, etc.
• Inferential statistics: this involves drawing inferences or conclusions from the collected data. Even though only a sample of the population was studied, such inferences or conclusions are binding on the entire population. Inferential statistical methods include a test for a significant difference between means, the test of goodness of fits, correlation, regression, etc. Raw data might need some forms of descriptive statistical arrangement before suitable inferential statistical methods are applied.

TYPES OF DATA IN BIOLOGY

Biological data can be classified on two bases:

1. Source the data
2. Form of data

On the basis of source, data can be

• Primary
• Secondary
• Tertiary

On the basis of form, data can be

• Categorical
• Quantitative

Primary data are those data that are collected directly by the researcher himself/herself irrespective of the method used in the collection. Secondary data are unpublished data from other research that are collected during research while tertiary data are those obtained from published sources. Primary data in the hands of one researcher is secondary data in the hands of another. Those collecting secondary data should, however, inquire about the mode of collection of the data to determine the suitability of such that for their own study.

Categorical data are observation recorded in categories, classes, or groups. Such categorization can either be descriptive or ranked. An example of descriptive categorical data includes skin colour (dark, chocolate, fair) while that of ranked include grade (first class, second class, etc).

Quantitative data are observations recorded as numerical quantities. They can either be continuous or discontinuous. Continuous quantitative data can take any form; be it whole number or fractions. E.g. weight of individuals. Discontinuous quantitative data can only take discrete or whole number forms. E.g. number of teeth of individuals.