The Practical Stats Webinar
NADA2: Everything you can do today with nondetects

The recording will soon be located on
our Online Training Center
along wth a pdf of the Powerpoint images and Questions with Answers for Q's submitted.

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During / After the webinar: click to Ask A Question (reply will be within 2 days)

What statistical analyses can you do today for data with nondetects without substituting numbers like ½ the detection limit? It is essentially every analysis you do when there are no nondetects. Of course there's estimating means and other descriptive statistics. You may have moved on to computing confidence intervals on those statistics. But there's much more. In our NADA online course right now, and coming in early 2021 as the NADA2 package for R, are routines for drawing (while incorporating the information in nondetects) boxplots, scatterplots with fitted models, and probability plots to determine how well a standard distribution such as the normal, lognormal or gamma fit the data. You can compute prediction and tolerance intervals, or perform hypothesis tests (parametric, nonparametric and permutation varieties). Follow that up with multiple comparison tests to determine which groups differ from others. You can compute correlation coefficients, build and evaluate regression models using AIC or other statistics to find the best model. You can perform trend analysis such as the seasonal Kendall test while adjusting for the effect of exogenous variables that are not time. You can even compute multivariate procedures such as cluster analysis, NMDS plots, PCA (Principal Components Analysis) and multivariate group and trend tests.

Are you still substituting fractions of the detection limit? You're likely getting back incorrect answers. Are you dropping variables because they have 'too many' values below detection limits? You're very likely to be missing important patterns and relationships. With all the money and effort it takes to plan a project, collect and chemically analyze your data, why stick in a fabricated number that you or someone else made up? Why delete data that was expensive to collect and analyze? Let me show you what is possible with modern software and stop limiting yourself to simplistic and cheap (quality) methods of data analysis.

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