1. MARLAP Chapter 20 Detection and Quantification Limits Keith McCroan Bioassay, Analytical & Environmental Radiochemistry Conference 2004

2. Overview Chapter 20 of MARLAP deals with issues of analyte detection as well as measures of detection capability and quantification capability It makes recommendations about the proper use of the critical value and minimum detectable value It offers suggestions for how to calculate them in various situations

3. Overview - continued Chapter 20 also presents a concept that has tended to be ignored by radiochemists: the minimum quantifiable value We recommend that radiochemists become familiar with this concept and use it when it is appropriate The chapter suggests equations to calculate it

4. Hypothesis Testing The theory of analyte detection for radiochemistry is derived from statistical hypothesis testing In a hypothesis test, one constructs two mutually exclusive hypotheses The null hypothesis: There is no analyte in the sample The alternative hypothesis: There is some analyte in the sample

5. The Null and Alternative Hypotheses The null hypothesis is presumed to be true unless there is good evidence to the contrary (like presumption of innocence in a criminal trial) The alternative hypothesis is accepted if the null hypothesis is rejected (like finding the defendant guilty)

6. Decision Errors Measurements aren’t perfect — There is always uncertainty Regardless of which hypothesis is true, there is a chance of choosing the wrong one Choosing the wrong hypothesis is a decision error

7. Type I Errors A type I error (false rejection, false positive) is the decision error made by rejecting the null hypothesis when it is actually true One specifies the type I error rate, , that one considers tolerable This error rate is called the significance level By default,  is often taken to be 0.05

8. The Critical Value In hypothesis testing, the critical value is the threshold value for the test statistic that is used to choose between the null and alternative hypotheses The term has been borrowed, with only a slight change in definition, to denote the threshold value to which a measured value is compared in the lab to make a detection decision

9. The Critical Value - continued One may base the detection decision on the gross count, net count, gross count rate, net count rate, or even on the final calculated activity There is an associated critical value for each of these (e.g., the critical net count rate or the critical gross count)

10. The Critical Value - continued The critical value is selected to limit the type I error probability to  There are many possible equations for it Choose one that is appropriate for your situation: One size does not fit all MARLAP does not prescribe any particular equation for the critical value

11. Other Names Critical level (Currie 1968) Decision level (ANSI N42.23, N13.30)

12. Type II Errors A type II error (false acceptance, false negative) is the decision error made by failing to reject the null hypothesis when it is actually false The probability of a type II error depends on the amount of analyte in the sample Generally the type II error rate, , goes down as the concentration of analyte goes up (more statistical “power”)

13. The Minimum Detectable Value The minimum detectable value is the smallest value of the analyte that ensures the type II error probability is no more than a specified value of , which is often 0.05 by default MARLAP generally refers to the minimum detectable concentration (MDC), on the assumption that the measurand is usually the concentration of analyte in a sample Some say the minimum detectable activity

14. Detection Capability Detection capability is the term used by MARLAP to mean the ability of the measurement process to detect the analyte (i.e., to avoid type II errors) The MDC is a measure of detection capability

15. The Method Detection Limit Beware: MDL ≠ MDC It is unfortunate that the abbreviations are so similar — the similarity causes confusion The regulation (40 CFR 136, App B) says the MDL is to be used as a critical value, but it is also used as measure of detection capability

16. Misuse of the MDC A common mistake is to make a detection decision by comparing a measured result to the MDC Make the detection decision by comparing a result to the critical value, not the MDC The definition of the MDC presupposes that an appropriate detection criterion (the critical value) has been chosen

17. Misuse of the MDC - continued The type II error rate for a sample whose concentration is at the MDC is supposed to be small (usually  = 0.05) If you base your detection decision on the MDC instead of the critical value, then the type II error rate at the MDC will be about 50 % Dumb mistake, but smart people make it

18. Low-Background Poisson Counting Attachment 20A compares several published approaches for making detection decisions based on Poisson counting (similar to Strom and MacLellan’s work) It’s fun to explore the mathematics of Poisson counting, but beware: the actual data distribution is usually not pure Poisson The pure Poisson model is appropriate in relatively few situations (e.g., alpha spec analysis for certain analytes)

19. Quantification Capability Quantification capability refers to the ability of a measurement process to measure the analyte with good relative precision It may be expressed as the minimum quantifiable value (e.g., minimum quantifiable concentration, or MQC)

20. The Minimum Quantifiable Value The minimum quantifiable value is defined as the value of the analyte for which the relative standard deviation of the measurement has a specified value, usually 1/10 MARLAP treats random errors and systematic errors alike when estimating this relative standard deviation – much like the ISO-GUM approach to uncertainty propagation

21. Recommendations When a detection decision is necessary, make it by comparing the signal or measured result to its critical value The lab should choose appropriate expressions for the critical value and MDC The client should not specify the equations without detailed knowledge of the measurement process

22. Recommendations - continued Use an appropriate radiochemical blank to predict the signal produced by an analyte- free sample It might be a series of reagent blanks It might be only a blank source Whatever works

23. Recommendations - continued Confirm the validity of the assumption of Poisson statistics before using expressions for the critical value and MDC that are based on the Poisson model Consider all sources of variability in the count rate when calculating critical values and MDCs – not just counting statistics

24. Recommendations - continued Use the MDC only as a performance characteristic of the measurement process Never compare a result to the MDC to make a detection decision The lab should report each result and its uncertainty even if the result is less than zero – Never report “< MDC” However, we don’t presume to tell the lab’s clients how to present data to others

25. Recommendations - continued Don’t use the MDC for projects where the issue is quantification of the analyte and not detection (e.g., 226 Ra in soil) For such projects, MARLAP recommends the MQC as a more relevant performance characteristic of the measurement process

26. Questions?