AJ, you're trying to teach Grandpa how to suck eggs.
Apparently Grandpa needs a refresher on some fundamental concepts.
I already know that you don't really understand this stuff, and that was evident earlier in your misunderstanding of reliability and validity,
Taking validity first we note that in a triangle test we can observe the number of correct answers and relate it to the parameter of interest by Pd = (Pc -1/k)/(1-1/k). Thus Pc is a valid thing to test in order to get and estimate of the state variable of interest. Now with respect to reliability, we know that people have self noise and biases. We note that it is important in conducting a triangle test to minimize those noises and biases and we know ways to decrease the extent to which that noise degrades the estimate. It seems we do know about reliability and validity after all but statisticians in the real sciences, engineers and physicists may be more likely to discuss measurability, ergodicity and stationarity rather than in terms of validity and reliability.
..and now in your statement that a result would be significant if a larger panel were used. That's a game, set, match type of comment.
Yes it is as the truth of my statement is just common sense. Or at least it is to someone who has spent as much time as I have looking at data corrupted by noise. I have explained how this works and even given a chart that shows expected significance vs panel size parametric in Pd. You are saying that the chart is invalid and are claiming it to be so because I am a tyro when it comes to this stuff. Your argument would be a lot more convincing if you could critique my method or my results (which would also require you to explain why Table A1.1 in ASTME1885, which shows better confidence with increased panel size, is wrong.)
When, in another post, you asserted again that I didn't understand reliability and presented a sequence of widely varying weights as representative of unreliable data and I responded by showing you how to get 'actionable' information from it you chose to ignore it as you do with most of my rebuttals. And there are rebuttals in that case. If you had said that the scale's noise were non ergodic or non stationary then indeed the sequence would be unreliable. Had you said that I would have concluded that you know what you are talking about but as you just ignored the rebuttal I had to assume that you couldn't rebut my point and accept the alternate hypothesis.
And you present all kinds of handwaving to try to buttress....some sort of point.
There have been several rather distinct points
- Triangle tests are not inherently flawed. They can yield useful information to brewers
- The essence of the triangle test is forced guessing
- Increasing panel size improves the sensitivity of the test
- Improperly done triangle tests can lead one astray. Care is needed
- Increasing the order of the test from binary to triangle to quad increases the sensitivity
I don't see how you could have missed those as each was followed by analysis, graphs and numerical examples.
If I were not correct then you should be able to refute some of the points more solidly than just fuming that I am a tyro and thus have no right to express an opinion. But, as you can't seem to figure out what those points are obviously you can't do that. If you can't figure out what the points are how can you reasonably offer an opinion as to whether they are correct or not other than to claim that the reporter is globally disqualified? This is the approach you have taken and it is not one that leads to fruitful dialogue. And, ultimately, I am, of course, correct as I have justified my positions using sound principles most of which are found in fundamental texts on probability, statistics, estimation theory and so on. Furthermore, all the points except the last one above are supported by the fact that the triangle test is widely accepted and used by thousands of scientists. The third point is obvious.
..and so even if what you post *is* correct, it isn't a defense of what you wrote.
Huh?
I teach this material. I know it very, very well.
I think the basic problem here may be that you know one corner of statistics - the parts that are used in your field. Statistics is much, much broader than this. You seem unaware of much of the terminology and techniques of the other branches e.g. ROC's, SEM, SNR, entropy, differentiability. At least when I have asked if you are familiar with those terms you have ignored the question.
You are a journeyman when it comes to this.
Well thanks but I think you mean the opposite.
If this were a water thread, I'd bow to your knowledge, which appears to be very good.
Thanks for this compliment too but if you see me say something that doesn't make sense with respect to water (and I have done that) then I don't want you to bow to my experience. I want you to point out what you see as wrong. I'm smart enough to know that just because I have taught it for many years doesn't mean that I'm always right. In fact I've found that those with long experience (the acknowledged 'experts') are quite often wrong on certain points as our memories slowly and subtly drift over time.
I feel that I must point out that while I have been thinking about brewing water for perhaps 20 years I have been applying the statistical concepts I've advanced in this thread for more like 50 yrs to radar, sonar, signal processing, telemetry, communications, orbital mechanics, horology, navigation, antennas, pH measurement and color characterization. I should also point out that I have had very similar discussions with guys re water chemistry They make outrageous statements (e.g. Henry's coefficient is a function of pH) and when bombarded with evidence to the contrary fall back on "I've been teaching it for years. You don't know what you are talking about."
Your attempt to deflect explanation of the misunderstanding...
I have never attempted to deflect explanation of the misunderstandings. I have attacked them directly. I have, in all cases, tried to give you enough information to help you to do some thinking or pencil and paper work or Monte Carlo simulations or even just reading to resolve your misunderstandings. If you can remove the blinders I think you can do that. When you say that increasing panel size may result in the same or reduced significance that is true. You just need to understand that it is true proportionally less and less of the time as panel size increases so that the expected result (are you familiar with the term "expected value"?) goes down. You have to accept that the probable significance levels depends not only on the width of the null distribution but of the alternate distribution. A little thinking should make that obvious. Think rather than dismissing this as hand waving and you'll be there.
..is analogous to what I see people do in some fields, where if an analysis doesn't yield significance at the .05 level, they relax it to the .10 level.
My attempts to clearly explain the principles have nothing to do with changing the confidence levels. I have, though, demonstrated that if one obtains a marginal confidence level he is likely to get improved confidence by repeating the test with a larger panel (or going to a quad rather than a triangle).
In other words, we're going to show something matters even if we have to change the rules to make it so. I always laugh at that, and whenever I see it, I know what they know about statistics--not much.
I wouldn't assume that at all. My old boss used to say "Figures don't lie but liars figure."
You can keep posting charts and graphs until you're blue in the face, but that changes nothing--it just makes it appear, to the casual reader, that you in fact have an argument, when you do not.
Yes, there's nothing like data to mislead people especially when it agrees with published data and that's what I would hope readers would see: AJ's spreadsheet agrees with ASTM E1885 as do his curves thus either AJ is right or AJ and ASTM are both wrong.
It is particularly distressing to me that as of my reading of this post two people had 'liked' it. This means you have taken them in and I have not done a good enough job of stating the case.
None of this makes you a bad person, but in this area, you might find it advantageous to listen more and post less.
You have stated that triangle testing is invalid because it is a forced guessing test. That's wrong. You have implied that only someone who doesn't know anything about statistics would think increasing the panel size would improve the power of a triangle test. That's wrong too. Were I to remain silent there would be more than two people who would accept those incorrect statements.