Biostatistics Lecture (1) PDF

Biostatistics Lecture (1)  Statistics and Biostatistics Statistics: The study and use of theory and methods for the analysis of data arising from random processes or phenomena. The study of how we make sense of data The field of statistics provides some of the most fundamental tool...

Biostatistics Lecture (1)  Statistics and Biostatistics Statistics: The study and use of theory and methods for the analysis of data arising from random processes or phenomena. The study of how we make sense of data The field of statistics provides some of the most fundamental tools and techniques of the scientific method Forming hypotheses Designing experiments and observational studies Gathering data Summarizing data Drawing inferences from data testing hypotheses The field of statistics can be divided into Mathematical Statistics: the study and development of statistical theory and methods in the abstract. Applied Statistics: the application of statistical methods to solve real problems involving randomly generated data and the development of new statistical methodology motivated by real problems  Biostatistics is the branch of applied statistics directed toward applications in the health sciences and biology.  Data Data: are observations of random variables made on the elements of a population or sample Data are the quantities numbers or qualities attributes measured or observed that are to be collected and/or analyzed. The word data is plural datum is singular. A collection of data is often called a data set singular.  Variables A variable is an object, characteristic or property that can have different values in different places, persons, or things. A quantitative variable can be measured in some way. Examples: Heart rate, heights, weight, age, size of tumor, volume of a dose.  A qualitative (categorical) variable is characterized by its inability to be measured but it can be sorted into categories. Examples: gender, drug name, disease status.  Populations and Samples A population is the collection or set of all of the values that a variable may have. A sample is a part of a population. We use the data from the sample to make inference about the population The sample mean is not true mean but might be very close. Closeness depends on sample size.  Parameters vs. Statistics A parameter is a population characteristic. A statistic is a sample characteristic. Example: we estimate the sample mean to tell us about the true population mean. The sample mean is a ‘statistic’ The population mean is a ‘parameter’