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**Biostatistics** can be understood as the branch of statistics that tries to make sense of data pertaining to living organisms. Investigators and researchers apply the principles of statistics to solve problems and questions in the fields of biology, medicine and public health. For example, it can be used to answer questions such as whether a new drug is effective in the treatment of a certain disease, what is the survival rate or chance for patients of a type of cancer, etc. It is also used to analyze data and understand relationships between factors such as environmental or substance exposure and health outcome. Biostatisticians work using their quantitative skills to collaborate with experts from a wide range of research and practice areas such as geneticists, pharmacists and healthcare professionals including disease specialists and surgeons. They also play a crucial role in designing research by ensuring that appropriate amount of data and the right type of information are collected for the study. They then analyze, assess and interpret the results to derive meaningful conclusions while also factoring in the variables, missing data and biases all through the process

Biostatistics requires professionals to merge their skills and dedication with statistics, mathematics and biomedical science. It involves hypothesis generation, data collection and data analysis. Application of statistical tools and methods provides an understanding of important statistical issues as well as the underlying medical and biological context. Experts in the field believe that biostatics is central to all scientific research as they all need collecting appropriate evidences and evaluation of those evidences to make accurate judgments.

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Data can be defined simply as the collection of observations recorded during the research for statistical analysis. The type of statistical tools and methods to be used depends on the type of data. There are three types of data in biostatistics:

**Nominal Data:** Nominal data refers to the dataset which is assigned different names or categories on the basis of certain characteristics possessed by the individual observations without any ranking among the categories. For, example, a data of 100 participants in a research can be divided into separate categories based on gender. Where two possible outcomes are studied, the data is called binominal data. Studies of bacteria culture often use binominal data to categorize the growth of bacteria as positive and negative for a given medium of growth.

**Ordinal Data:** Also called categorical, ordered or graded data, ordinal data is generally presented as ranks or scores. The categories can be arranged on the basis of a natural order occurring among them. For example, speed may be categorized as slow, average and fast. As there is a natural order among the three categories, the data on speed can be called ordinal data. Scores can also be assigned to indicate the intensity of each category. Based on these criteria the data can easily be arranged in order or rank.

**Interval Data:** This type of data is characterized by a definite and equal interval occurring between two consecutive observations or measurements. For example, the weights of 1,2,3,4 and 5kgs can be categorized as interval data as the interval between all consecutive values (such as 1 and 2, 4 and 5) is the same. Interval data is further divided into continuous and discrete data. Simply understood, in continuous data, the variable may take any value including decimals within a specified range, whereas in discrete data the values must be whole numbers only. For example, the hemoglobin level can be recorded as 11.4, 12.6 or 13.8gm% thus the data is continuous, whereas the number of meals eaten by a person can by recorded as 1,2,3 etc. and not 1.5, 2.4 or 3.8 which means that the data is discrete.

Distribution refers to the pattern in which the observations or variables are distributed across the data. Though there are many uncertainties and variations in the observed values, the inferences from biological and experimental observations are always inclined towards a normal distribution. The two types of distribution applicable in biostatistics are:

** Normal or Gaussian Distribution:** The normal/Gaussian distribution system was developed by noted Brazilian statistician Prof. Gauss M. Cordeiro. The data distribution is called normal or Gaussian if the variables are distributed equally or symmetrically on both sides of the mean value. The data here forms a bell-shaped curve on the frequency distribution plot in which the skew is zero. It is used to describe the ideal or normal distribution of continuous values and is commonly used to express data of heart rate, blood sugar levels, etc. in a normal distribution of variable values; the mean, median and mode are equal for the population being studied. Common tests to evaluate normality of the distribution include Analysis of Variance (ANOVA) and t-tests.

**Non-normal/Non-Gaussian Distribution:** The data distribution is called non-normal or non-Gaussian if it is skewed on one side of the mean. In such a case the data may be binominal or Poisson where there is a high probability that the outcome can either be mostly positive or negative (such as the efficacy of a drug). Based on the nature of the data, several tests including Mann-Whitney, Wilcoxon and the Friedman test can be applied to analyze non-normal distribution.

The **measure of central tendency** is a single value which describes the core attribute of the dataset by determining the central position within the collection of observations. Measures of central tendencies are also referred to as measures of central location and summary statistics. There are three measures of central tendencies:

**Mean:** It is the most common measure of central tendency widely used to calculate average values. It is used mostly to calculate the average in continuous data even though it can be used for discrete data as well. The mean or average is calculated by dividing the sum of all individual variable values by the number of variables in the dataset. Least affected by the fluctuations in sampling, the mean is always the closest to the actual values of all individual variables than the other two measures of central tendencies. Due to the low variation than the individual values, mean provides the researcher with high level of confidence. A major drawback of the mean is that it can be highly influenced by outliers. A couple of unusually large or small values can skew the mean to express a value that does not do justice to other normal values in the dataset.

**Median:** It is the central value of the dataset obtained by arranging the variables in a specific order either from highest to the lowest or from lowest to the highest value. Compared to the mean, the median is less affected by skewed data and outliers.

**Mode:** It is the most frequently occurring value in the dataset. Mode is normally used for categorical data in which the investigator has to identify the most common category. For example it can be used to determine the most preferred mode of transportation for a sample population of daily commuters. Mode is not an effective measure of central tendency because it is not unique which means that there can be more than one mode for a single dataset. It is also not effective to assess small set of continuous data where the probability of two exact same values such as weight is very low.

While there are many research areas in biology, medicine and public health in which biostatistics is used, the most important and common applciations include:

Identifying and developing treatments for diseases and measuring their efficacy.

Identifying risk factors and environmental variables that cause diseases.

Designing, monitoring, assessing, describing and reporting the results of research and clinical studies.

Developing statistical methods to answer research questions related to medical and public health data.

Locating, measuring and defining the extent of diseases.

Identify the ways in which public health can be improved.

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