Terminologies in Biostatistics

1. COMMON TERMINOLOGIES USED IN BIOSTATISTICS
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1.Statistics: is a branch of mathematics that involves the collection, analysis,
interpretation, presentation, and organization of numerical data. It provides methods
for summarizing and making inferences from data, enabling researchers to draw
conclusions and make informed decisions.
An example of statistics in action is analyzing the heights of a group of people. Let's
say we collect the heights of 100 individuals and want to understand the average
height and the variation within the group. We can calculate various statistical measures
such as the mean (average), median (middle value), and standard deviation (measure
of spread) to gain insights into the data.
By using statistics, we can determine the average height of the group, understand the
range of heights, identify any outliers, compare individual heights to the group
average, and make predictions or generalizations about the entire population based
on the sample data. These statistical techniques help us make sense of data, draw
meaningful conclusions, and support evidence-based decision-making processes in
various fields such as science, economics, psychology, and more.
2. Biostatistics: is a specialized field of statistics that applies statistical methods and
principles to biological and health-related data. It involves collecting, analyzing,
interpreting, and presenting data in various biological and medical research studies.
Biostatistics plays a crucial role in understanding patterns, making predictions, and
drawing meaningful conclusions in the field of life sciences.
An example of biostatistics in action is a clinical trial evaluating the effectiveness of a
new drug. Let's assume researchers are investigating a new medication for a particular
disease. They design a randomized controlled trial where they recruit two groups of
participants: one receiving the new drug (treatment group) and the other receiving a
placebo or standard treatment (control group).
3.Attribute: an attribute refers to a characteristic or variable that describes a particular
individual, object, or event being studied. It represents a specific aspect or quality that
can be measured or categorized.
Attributes can be qualitative (categorical) or quantitative. Qualitative attributes
represent non-numerical characteristics, such as gender or brand preference, which
can be classified into distinct categories. Quantitative attributes, on the other hand,
involve numerical measurements, such as age or income level, which can be further
analyzed using various statistical techniques.
4. Variable: refers to any characteristic, quantity, or feature that can vary or take on
different values. It is a factor that is being observed, measured, or manipulated in a
study or experiment.
For example, in a study on the effects of exercise on heart rate, the heart rate is the
variable of interest. It can vary from person to person and can be measured at different
time points during exercise or rest.

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5.Data: refers to the collection of observations, measurements, or information
gathered from a specific population or sample. It represents the raw, unprocessed facts
or values that serve as the basis for statistical analysis and inference.
6. Descriptive statistics: refers to the branch of statistics that involves summarizing,
organizing, and presenting data in a meaningful and informative manner. Descriptive
statistics deal with various measures that provide a summary of the data's central
tendency, variability, and distribution.
E.g. the mean (average), median (middle value), mode (most frequent value)., range
(difference between the maximum and minimum values), variance (average squared
deviation from the mean), and standard deviation (square root of the variance).
percentiles, quartiles, and graphical representations like histograms or box plots.
7. Inferential statistics: is a branch of statistics that involves making inferences or
predictions about a population based on a sample of data taken from that population.
The primary goal of inferential statistics is to conclude a population from a
representative sample, as it is often impractical or impossible to study an entire
population.
E.g. Hypothesis Testing, Confidence Intervals, Regression Analysis etc.
8. Parameters: are characteristics or measures that describe a population. They are
numerical values that summarize important features of a population, and they are
often used to make inferences about the population based on a sample.
E.g. mean, standard deviation, and proportion
9. Population: The complete set of individuals, objects, or events that possess the
characteristics under investigation.
10. Sample: A subset of the population that is selected for the actual study. The goal
of taking a sample is often to make inferences or draw conclusions about the entire
population based on the characteristics observed in the sample.
For example, if you are interested in studying the average income of all households in
a country, the population would be all those in that country. However, it might be
impractical or impossible to collect data from every single household, so you might
select a sample of households to represent the entire population.
11. Discrete variable: a type of quantitative variable in statistics that can only take on
distinct, separate values. These values are typically integers and cannot be subdivided
into smaller units.
A discrete variable can only assume specific, separate values. These values are often
counted in whole numbers and do not have intermediate values.
Examples of discrete variables include:

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The number of students in a class.
The number of cars in a parking lot.
The number of goals scored in a soccer game.
The number of defects in a manufacturing process
12. Continuous variables: are quantitative variables that can take on an infinite
number of values within a given range. continuous variables can have any value within
a specified interval. These variables are often measured with high precision and can
include fractional or decimal values.
Examples of continuous variables include:
Height of individuals.
Weight of objects.
Temperature (e.g., Celsius or Fahrenheit).
Time taken to complete a task.
Distance travelled.
13. Dependent variable: is the variable that is being studied and measured. It is the
outcome or response variable that researchers are interested in understanding or
predicting. The values of the dependent variable depend on, or are influenced by, the
independent variable(s), which are the variables that are manipulated or controlled in
an experiment or study.
In a cause-and-effect relationship, the dependent variable represents the effect or
outcome, while the independent variable represents the cause or factor that is believed
to influence the dependent variable.
For example, in a study examining the effects of a new drug on blood pressure, blood
pressure would be the dependent variable. The independent variable would be the
administration of the drug, with researchers interested in understanding how changes
in the independent variable (drug dosage) affect the dependent variable (blood
pressure).
14. The independent variable: is a variable that is manipulated or controlled in an
experiment or study. It is the variable that researchers believe affects the dependent
variable. The independent variable is what researchers change or vary to observe its
impact on the dependent variable.
For example, in a study investigating the effect of sunlight exposure on plant growth,
the amount of sunlight (measured in hours per day) would be the independent
variable. The researcher might expose one group of plants to 4 hours of sunlight per
day, another group to 8 hours, and a third group to 12 hours. The growth of the plants
(dependent variable) is then observed and measured.

4. COMMON TERMINOLOGIES USED IN BIOSTATISTICS
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15. Confounding variables: are extraneous factors that may interfere with the
relationship between the independent variable and the dependent variable, leading to
a misinterpretation of results.
Example: Coffee Consumption and Heart Health
Independent Variable: Coffee consumption.
Dependent Variable: Heart health.
Confounding Variable: Smoking habits.
If a study finds a correlation between high coffee consumption and poor heart health,
it might be confounded by the fact that heavy coffee drinkers also tend to be smokers.
Smoking is a known risk factor for heart problems, and it could be the confounding
variable affecting the results.