Permutation Tests and Bootstrapping
Never worry about a normal distribution again!
Traditional parametric tests for differences in means (Analysis of Variance, t-tests and more) as well as t-intervals require data within groups to follow a normal distribution. If this isn't so, p-values may be inflated so that differences in means are not detected, and confidence intervals are often too wide. Permutation tests and bootstrap intervals avoid the normality assumption, returning accurate p-values and interval widths while being distribution-free. These methods are widely used in a variety of applied statistics fields including environmental science, but have not been sufficiently used in water quality, air quality and soils applications. This webinar will describe how these methods work, where you can find them, and demonstrate their benefits over older traditional methods.
Dennis Helsel (Ph.D. Environmental Science and Engineering):
"I'm a translator of statistical methods for scientists. My firm's name, Practical Stats, says it all. I've written/co-authored two textbooks. Statistics for Censored Environmental Data using Minitab and R (2012) pioneered statistical methods for data below detection limits. Statistical Methods in Water Resources (2020) has been cited by scientists throughout the world. I've taught webinars for the National Water Quality Monitoring Council and others; workshops for the American Statistical Association and others; courses such as AES and NADA for scientists in North America, Europe and Singapore since 1990. I worked for 30 years at the US Geological Survey before starting Practical Stats.
In 2003 I received the Distinguished Achievement Award from the American Statistical Association’s Section on Statistics and the Environment for my training courses in applied statistics.
In 2018 I received the Lifetime Achievement Award from the California Groundwater Resources Association for my communication of statistical methods to scientists. My courses provide up-to-date procedures communicated in clear language using video examples of data analysis of 'real data'."