Biostatistics?

Topics Covered in the Presentation Measurement of Central Tendencies Mean

What is Biostatistics? Biostatistics is the branch of statistics responsible for the proper interpretation of scientific data generated in the biology, public health and other health sciences (i.e., the biomedical sciences). In these sciences, subjects (patients, mice, cells, etc.) exhibit considerable variation in their response to stimuli. This variation may be due to different treatments or it may be due to chance, measurement error, or other characteristics of the individual subjects. Biostatistics is particularly concerned with disentangling these different sources of variation. It seeks to distinguish between correlation and causation, and to make valid inferences from known samples about the populations from which they were drawn. (For example, do the results of treating patients with two therapies justify the conclusion that one treatment is better than the other.

Biostatistics is a branch of applied statistics and it must be taught with the focus being on its various applications in biomedical research. Biostatistics is a broad discipline encompassing the application of statistical theory to real-world problems, the practice of designing and conducting biomedical experiments and clinical trials (experiments with human subjects), the study of related computational algorithms and display of data, and the development of mathematical statistical theory. Biostatistics is integral to the advance of knowledge in biology, health policy, clinical medicine, public health policy, health economics, proteomics, genomics, and other disciplines.

Applications of Biostatistics Public health, including epidemiology, health services research, nutrition, environmental health and healthcare policy & management. Design and analysis of clinical trials in medicine Assessment of severity state of a patient with prognosis of outcome of a disease. Population genetics, and statistical genetics in order to link variation in genotype with a variation in phenotype. This has been used in agriculture to improve crops and farm animals (animal breeding). In biomedical research, this work can assist in finding candidates for gene alleles that can cause or influence predisposition to disease in human genetics Analysis of genomics data, for example from microarray or proteomics experiments. Often concerning diseases or disease stages. Ecology, ecological forecasting Biological sequence analysis Systems biology for gene network inference or pathways analysis

Central Tendency In statistics, a central tendency (or measure of central tendency) is a central or typical value for a probability distribution. It may also be called a center or location of the distribution. Colloquially, measures of central tendency are often called averages. The term central tendency dates from the late 1920s. The most common measures of central tendency are the arithmetic mean, the median and the mode. A central tendency can be calculated for either a finite set of values or for a theoretical distribution, such as the normal distribution. Occasionally authors use central tendency to denote "the tendency of quantitative data to cluster around some central value. Measures of central tendency are numbers that tell us where the majority of values in the distribution are located.

Measures of central tendency The following may be applied to one-dimensional data. Depending on the circumstances, it may be appropriate to transform the data before calculating a central tendency. Arithmetic mean (or simply, mean) – the sum of all measurements divided by the number of observations in the data set. Median – the middle value that separates the higher half from the lower half of the data set. The median and the mode are the only measures of central tendency that can be used for ordinal data, in which values are ranked relative to each other but are not measured absolutely. Mode – the most frequent value in the data set. This is the only central tendency measure that can be used with nominal data, which have purely qualitative category assignments. Geometric mean – the nth root of the product of the data values, where there are n of these. This measure is valid only for data that are measured absolutely on a strictly positive scale. Harmonic mean – the reciprocal of the arithmetic mean of the reciprocals of the data values. This measure too is valid only for data that are measured absolutely on a strictly positive scale. Measures of central tendency

Mean (Arithmetic) The mean (or average) is the most popular and well known measure of central tendency. It can be used with both discrete and continuous data, although its use is most often with continuous data. The mean is equal to the sum of all the values in the data set divided by the number of values in the data set. So, if we have n values in a data set and they have values x1, x2, ..., xn, the sample mean, usually denoted by (pronounced x bar), is: This formula is usually written in a slightly different manner using the Greek capitol letter, , pronounced "sigma", which means "sum of...":

Question-Find the average height.

Statisticians generally use the arithmetic mean as a measure of central tendency for numbers that are from a ratio scale (e.g., many biological values, height, blood sugar, cholesterol), from an interval scale (e.g., Fahrenheit temperature or personality measures such as depression), or from an ordinal scale (high, medium, low). The values may be either discrete or continuous; for example, ranking on an attitude scale (discrete values) or blood cholesterol measurements (continuous).

References https://medschool.vanderbilt.edu/biostatistics/content/what-biostatistics