#### Videos on Applied Environmental Statistics

These videos introduce several topics covered in what was our long-standing Applied Enviornmental Statistics course, which we've taught since 1990. Videos from the AES course are not on our webistie, but you'll find most of the same information in the free textbook Statistical Methods in Water Resources (2020), available from the US Geological Survey at:.https://doi.org/10.3133/tm4A3 . All of the graphs and examples in that book are computed using R software and all the code is provided for you there.

###### NEW! Two Things I've Learned After 43 Years of Applying Statistics to Environmental Science
I talk about the two major principles I've learned on how to make sense of the data you've collected. First, use methods that fit your data and objectives, instead of hoping that the introductory methods from a brief class several years ago will be sufficient. This drives the type of methods you choose to use. Second, use methods that incorporate non-detect values directly. Non-detects are 'real data' -- there is no need to fabricate numbers such as 1/2 the detection limit for them. A demo of several methods (including regression) that work directly with nondetects is given.

approx. 45 mins

pdf of Powerpoint slides

###### 1. Intro to R
Break down the barrier of how to get started using R! Our AES course is also an introduction to using R software.
R is one of the most widely used statistics software packages in the world. Its versatility as a programming language and its interconnectivity with email, web page generation and other computer processes make it a bit daunting for people just starting to use it for data analysis. It need not be that way. This webinar introduces you to R software and its use for data analysis. You'll learn how to type commands, install and load packages, and use the pull-down menus of R Commander (Rcmdr) to compute confidence intervals and a test for whether the mean exceeds a numerical standard.

approx. 64 mins

###### 2. Never Worry About A Normal Distribution Again!
Permutation Tests and Bootstrapping
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.presented in this webinar.

approx. 60 mins

3. Which of These Things is Not Like the Others?
How Multiple Comparison Tests Work
Multiple comparison tests determine which groups differ from others. Why are they needed following an ANOVA or Kruskal-Wallis test? How do they work? There are familiar types such as Tukey's test, and a newish version called the False Discovery Rate. Learn why the False Discovery Rate is a method you should probably be using.

approx. 50 mins

###### 4. Forty Years of Water Quality Statistics: What's Changed, What Hasn't?
An overview of how methods have changed from 1980 - 2019 in interpreting water quality data. Some folks are still using methods from the era of rotary-dial phones. You've upgraded your phone. How about updating your statistical methods?

approx. 65 mins

###### 5. How Many Observations Do I Need?
One of the most common questions I am asked is “How many observations do I need to compute a confidence interval or find a difference in a hypothesis test?” To answer this you'll need to know quite a bit of information first. This webinar will go over what information is needed for two-group parametric and nonparametric hypothesis tests (t-test and Wilcoxon rank-sum test). More information is provided in the new Second Edition of Statistical Methods in Water Resources [published by the US Geological Survey and available here].

approx. 60 mins

###### 6. Seven Perilous Errors in Environmental Statistics
Seven common errors to avoid!
Seven common errors in statistical analysis by environmental scientists all stem from an outdated understanding of statistics. I'll define the seven 'perilous errors' and how each can be avoided. They revolve around old ideas about hypothesis tests, p-values, using logarithms of data, evaluating what is a good regression equation, evaluating outliers and dealing with nondetects. Understanding why each error is perilous can save the scientist from publishing incorrect statements, using inefficient analysis methods, and wasting scarce financial resources. These errors have persisted through the years -- break the cycle and step into the 21st Century.

approx. 63 mins (9 minutes per error!)