Biostatistics

Study guide covering scales of measurement for the Biostatistics course, a foundational topic for understanding how to classify and analyze different types of variables. The document breaks down the four measurement scales through a staircase model, detailed explanations, and a worked classification table that distinguishes interval vs. ratio variables—the most commonly confused distinction in exams. Includes a summary and practical examples of variable types that regularly appear in exam ques...

Lecture notes on data classification for Biostatistics covering the fundamental distinction between categorical and non-categorical data types. Topics include nominal data, ordinal data, discrete data, and continuous data, with zoological examples throughout and a practical two-question classification method for exam preparation. These notes provide clear definitions, key characteristics, and worked examples that make data type classification straightforward for exams.

Lecture notes from the Biostatistics course covering fundamental concepts of population, sample, and variables. The document explains finite vs. infinite populations, sampling principles and representativeness, and the critical distinction between qualitative (categorical) and quantitative (numerical) variables with zoology examples. Essential foundational material for understanding statistical methods in biostatistics—these notes break down core concepts clearly and provide practical examples...

These lecture notes introduce the scope and importance of statistics in biostatistics, covering foundational concepts essential to the course. The document explains where statistics is applied across different fields, outlines seven key reasons why statistics matters to researchers and decision-makers, and demonstrates how scope and importance work together to establish statistics as a foundational scientific discipline. Well-structured for exam preparation and ideal for understanding the core p...

Lecture 1 notes for the Biostatistics course covering foundational concepts and the complete framework of statistical analysis. Topics include the definition of statistics, biostatistics and its applications in research, and the two major branches—descriptive statistics (mean, median, standard deviation, charts) and inferential statistics (hypothesis testing, confidence intervals, ANOVA). Essential for understanding core biostatistics principles and exam preparation on the descriptive vs. infe...