Knowledge in statistics is essential to draw solid conclusions from data. This chapter covers the basic concepts needed to navigate in the world of biostatistics, with the aim that the reader should know what to search for when encountering new situations. First, the chapter introduces basic concepts such as scales of measurement, relation between sample and population, and features of stochastic variables such as the expected value and variance. Second, the chapter introduces various metrics for sample characterization, such as the mean and median for the central tendency, and sample variance, median absolute deviation, and interquartile range for characterizing variation. This also includes methods for visualization. Third, the chapter introduces the basics of hypothesis testing: construction of a null hypothesis, definition of a test statistic, and formulation of a test for significance. Both parametric and non-parametric hypothesis tests are explained, as well as tests for correlation. Multivariate regression and the F-test are briefly covered, as well as analysis of survival data. Finally, the chapter covers the perils of hypothesis testing including issues such as statistical power, type I and II errors, and the multiple-comparison problem.
|Title of host publication||Handbook of Nuclear Medicine and Molecular Imaging for Physicists|
|Subtitle of host publication||Modelling, Dosimetry and Radiation Protection, Volume II|
|Number of pages||16|
|Publication status||Published - 2022|
Subject classification (UKÄ)
- Probability Theory and Statistics