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Objectives of this articleTo understand

Read the next part of this article: Introduction to Biostatistics part 2
Statistics is defined as a body of processes for creating reasonable and wise decisions in the face of uncertainty. These are applied in the analysis of numerical data of various aspects including interpretation of data based on certain statistical principles.
Statistics is a field of study involving techniques or methods of collecting data, classification, summarizing, interpretation, drawing, inferences, testing of hypotheses, making recommendations, etc. only when a part of data is used.
Biostatistics is the term referred to when tools of statistics are applied to the data that is derived from biological sciences. In other words, when the principles of statistics are applied to the study of organisms or living systems, the study is called biostatistics or biometry.
It encompasses the design of biological experiments, especially in medicine, pharmacy, agriculture, and fishery; the collection, summarization, and analysis of data from those experiments; and the interpretation and inference from the obtained results.
Reference
In statistical language, any character, characteristic, or quality that varies is called a variable.
A characteristic that takes on different values in different persons, places, or things such as height, weight, blood pressure, age, etc is variable. It is denoted usually as “x”. Variables can be of two types: Categorical & Numerical variables.
A random variable is a variable whose value is a numerical outcome of a random phenomenon. For example: Flip three coins and let x represent the number of heads. Here, x is a random variable.
A random variable is not a probability. Its value doesn’t need to be positive or between 0 and 1 as in the case of probability.
Numerical variables are divided into two categories:
Qualitative Data are classified by counting the individuals or things having the same characteristic or attribute; and not by measurement. Examples:
Individuals with the same characteristics are counted to form specific groups or classes.
Qualitative data are discrete, such as the number of deaths in different years, the population of different towns, and persons with different blood groups in a population.
A continuous variable is a variable that has an infinite number of possible values. In other words, any value is possible for the variable.
A continuous variable doesn’t have to have every possible number (like infinity to +infinity), it can also be continuous between two numbers, like 1 and 2. For example, data of a discrete variable could be 1, 2 while the continuous variables could be 1, 2 and also everything or anything in between: 1.00, 1.01, 1.001, 1.0001…
Examples
The weight of students from 2nd year are (in kg) 40.9, 45, 55, 50.1, 53, 54, 54, 48, 48.5, 46, 70, 85, 82, 83.1, 62.5 etc.(See how the number varies within a range)
In the case of quantitative/continuous data, there are two variables the characteristics such as height & frequency. We find the characteristic, as well as the frequency both, vary from person to person as well as from group to group.
The quantitative data obtained from characteristic variables (e.g. height of individuals in 2nd year) are called continuous data because each individual has one measurement from a continuous spectrum or range.
Some of the statistical methods employed in the analysis of quantitative data are mean, range, standard deviation, coefficient of variation, etc.
References
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