Description
This set of university notes provides a foundational introduction to Biostatistics, authored by Dr Bornwell Sikateyo, PhD. The material is designed for tertiary-level students pursuing degrees, diplomas, or certificates in public health, medicine, biomedical sciences, or related professional programmes. It covers essential learning objectives, including the definitions of statistics and biostatistics, different types of variables (continuous, categorical, ordinal, nominal, dichotomous), and the key distinction between descriptive and inferential statistics. The notes offer detailed explanations and calculations for measures of central tendency such as mean, median, and mode, as well as measures of variability including range, variance, standard deviation, and interquartile range. This resource is crucial for students and lecturers seeking authoritative revision material to master biostatistical concepts, directly supporting exam preparation and academic success in Zambian colleges and universities. Download these comprehensive notes to strengthen your analytical skills for health research and data interpretation.
What's inside
INTRODUCTION TO BIOSTATISTICSDr Bornwell Sikateyo, PhD Dr Bornwell Sikateyo, PhD Learning objectives At the end of this lecture, you should be able to: Define statistics and biostatistics Define variable and random variable Differentiate different types of variables Distinguish between descriptive and inferential statistics Calculate means, medians, range, and standard deviations which are measures of central tendency and variability Introduction to Biostatistics: Definition of Statistics Collection, organization, presentation, analysis, and interpretation of numerical data to assist decision-making The application of statistics to the public health (Biomedical) field is known as Biostatistics Uses of Statistics Statistics presents a rigorous scientific method for gaining insight into data. For example, suppose we measure the weight of 100 patients in a study. With so many measurements, simply looking at the data fails to provide an informative account. However, statistics can give an instant overall picture of data based on graphical presentation or numerical summarization irrespective to the number of data points. Uses of statistics Besides data summarization, another important task of statistics is to make inference and predict relations of variables. Variables Variable -Any characteristic that can be measured or characterized Random variable - if each outcome of the variable can be determined by chance Why Statistics? Two Purposes 1. Descriptive Finding ways to summarize the important characteristics of a dataset 2. Inferential How (and when) to generalize from a sample dataset to the larger population Descriptive Statistics Statistics which provide graphical and numerical ways to organize, summarize, and characterize a dataset. Inferential Statistics Population: Is the totality of the observations of which we are concerned. Sample: Is part or a subset of a population. . Inferential statistics helps us make predictions or inferences about the population from the results of the sample. Inferential Statistics Population: The set of all individuals of interest (e.g. all women, all college students) Sample: A subset of individuals selected from the population from whom data is collected Inferential statistics Are the sample differences simply due to chance? No. of People 3.5 5.5 7.5 9.5 11.5 Nightly Hours of Sleep Women No. of People 3.5 5.5 7.5 9.5 11.5 Nightly Hours of Sleep Men Inferential Statistics Inferential statistics can tell us whether or not our results are likely to be due to chance alone
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