Biostatistics Basics and Data Presentation

Study Notes

Community dentistry is a challenging but scoring subject that includes topics like biostatistics, epidemiology, public health, and preventive dentistry.
Biostatistics is the study of health-related numbers, with Sir Ronald Fisher and Sir Francis Galton considered as fathers of biostatistics.
Biostatistics aims to convert data into relevant information for health research and is crucial in various health fields like public health, medicine, and population studies.
Basic terminologies in biostatistics include population (total focus group), sample (subset for study), parameters (characteristics studied), data (information collected), and variables (anything that varies).
Data can be qualitative (measured in categories) or quantitative (expressed in quantity), with qualitative data further classified as nominal or ordinal.
Qualitative data is descriptive and cannot be measured, while quantitative data can be discrete (whole numbers) or continuous (decimals).
Interval data is placed on a scale at equal intervals, while ratio data compares two continuous variables, and Likert scale is an exception considered qualitative but calculated as quantitative.
Variables can be qualitative (measuring quality) or quantitative (measuring quantity) and classified as independent (intervention) or dependent (outcome).
Data collection methods include primary (first-hand) and secondary (existing records) sources, collected through direct observation, house-to-house visits, or mailed questionnaires.
Data presentation can be done through text, tables, or graphs (like bar diagrams for qualitative data and histograms for quantitative data).- Malnutrition among children can be assessed by measuring height versus age, weight versus age, and height versus weight.
Height versus age and weight versus age are plotted using line diagrams because they involve time as a factor.
Height versus weight is plotted using scatter diagrams, indicating a correlational relationship between the x and y axes which are both quantitative.
In a scatter diagram, each individual is represented by a dot on the graph based on their height and weight.
The correlation between the x-axis and y-axis in a scatter diagram is expressed as a correlation coefficient (R) ranging from -1 to +1, where a higher value indicates a stronger correlation.
A correlation coefficient of 1 signifies a perfect positive correlation, while -1 indicates a perfect negative correlation, and 0 represents no correlation.
The strength of correlation is not determined by the sign (positive or negative) but by the absolute value, with values closer to -1 or +1 indicating stronger correlations.
Examiners may ask about the strength of correlation, emphasizing that values like -0.8 and +0.8 are equally strong correlations regardless of the sign.

Description

Learn about biostatistics basics including population, sample, data types, variables, and data collection methods. Also understand how to present data using text, tables, and different types of graphs. Explore the correlation between variables through scatter diagrams and correlation coefficients.