Profound Statistical Concepts

Profound Statistical Concepts 2019-10-31

Jim Wynne

Staff member
Admin
#21
ah but what you are ignoring is that it isn't that at all. there is no generalization going. With very few exceptions in scientific endeavors or in industrial quality we rarely just try to describe a population from a sample (what Deming called an enumerative study). What most of us attempt to do most of the time is to predict. (what Deming called an enumerative study). In science opinion doesn't matter at all; perhaps in politics, but not in science. science will always win. so I am not really sure of the point you are trying to make. Certainly there are many people who choose to not learn or think but the masses are no excuse for not doing the right thing.
One of the main things that causes science to go wrong is researchers trying to prove themselves right rather than trying to prove themselves wrong. The reason that the data always wins is that in those cases, someone will always be there to illuminate the fallacies. In industrial QA, we lack the safeguards of science in this respect, mainly because we're not interested in them. The emphasis is to give the customer what they ask for whether it makes any sense or not.
 
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Miner

Forum Moderator
Staff member
Admin
#22
On the other hand, industrial statistics has a self correcting mechanism that the other "sciences" lack. If you screw up in industry, the scrap and defect rates will set you straight faster than any peer review will.
 

Jim Wynne

Staff member
Admin
#23
On the other hand, industrial statistics has a self correcting mechanism that the other "sciences" lack. If you screw up in industry, the scrap and defect rates will set you straight faster than any peer review will.
In manufacturing, statistical analysis is generally intended to ameliorate scrap and defect rates, and misuse is more likely to be neutral than harmful insofar as production output is concerned. It either works or it doesn't, in other words. The primary untoward result of misuse of statistics is wasted time.
 

Bev D

Heretical Statistician
Staff member
Super Moderator
#24
respectfully, that continued scrap and the Customer experience effect for escapes is still a substantial cost and in some industries continued loss of life. when you add in the need for validating supply chain changes there is a real increase in cost and hazard...
 

Watchcat

Trusted Information Resource
#25
I and my 'students' have solved hundreds if not thousands of very complex problems and never calculated a p value or performed a null hypothesis test. We have applied the same approaches to new product development - quite successfully.
That's great, but it's not science. Science does not seek to solve problems or develop products; it simply seeks "knowledge" not to be confused with what people mean say they "know" something. (Nor does science seek truth.)

Simply saying the p value is less than .05 (a limit that Fisher pulled out of his back pocket with little to no thought at the dawn of statistics as a profession) without detailing the study design, including the sample sizes, and the underlying science is tantamount to scientific malpractice.
Agreed, but I think the malpractice usually comes in more on the other side of the equation. It is hard (although regrettably not impossible) to do a study without knowing the details of your study design and your sample size. The underlying science, that can be iffy. It is the accepting of a p-value alone that is the more common failing, IMO, whether it is practiced by journals or their readers. (Don't get me started on the media.)

While this behavior is quite common, it sounds like you have encountered virtually nothing else within your personal universe. In my universe, those who practice science well are few and far between, but not nonexistent.
 
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Watchcat

Trusted Information Resource
#26
With very few exceptions in scientific endeavors or in industrial quality we rarely just try to describe a population from a sample (what Deming called an enumerative study). What most of us attempt to do most of the time is to predict.
In my universe, many people do indeed try to use science to predict, but others simply use it to describe a population. This is the knowledge that is sought. Darwin used science to gather knowledge about the origin of the species, not to predict anything, as far as I know.
 

Watchcat

Trusted Information Resource
#27
One of the main things that causes science to go wrong is researchers trying to prove themselves right rather than trying to prove themselves wrong.
I prefer just "finding out," rather than proving right or wrong, although the latter is a better semantic fit with the notion of rejecting a hypothesis. I have the annoying habit of jumping in when someone says they are doing a test or study "to show that..." and saying, "No, we are doing that "to find out if..."
 

Ronen E

Problem Solver
Staff member
Moderator
#28
In the first paragraph you say that "QA professional" must only apply techniques without necessarily understanding the foundations of them, then in the second paragraph you say, ""People of very diverse levels of education and intellectual development" should not be drawing up experimental designs based on higher-than-undergrad-level statistical theory. Professional statisticians are there for that. " Leaving aside the apparent contradiction, you seem to think that the widespread practice of plugging numbers into Minitab and accepting the results uncritically is an acceptable state of affairs. If you're pretty sure that something works without a clue as to why it works, sooner or later something bad will happen.
Either you misunderstood me (I find it quite hard to believe, but the possibility exists) or you deliberately twisted my statements so as to match some image of me that you created in your mind and an unsound argument that that image (but not me) was trying to make. The latter isn't worth my time debating.
What you perceive as me thinking can't be further from reality.
 

Ronen E

Problem Solver
Staff member
Moderator
#29
I prefer just "finding out," rather than proving right or wrong, although the latter is a better semantic fit with the notion of rejecting a hypothesis. I have the annoying habit of jumping in when someone says they are doing a test or study "to show that..." and saying, "No, we are doing that "to find out if..."
According to Information Theory, the potential rate of acquiring new knowledge is maximized when the probability of all outcomes (in the case of testing a hypothesis, proving it right and proving it wrong) is exactly equal. So when an experiment is designed to show a certain expected outcome, and that outcome actually comes about, the experiment is highly inefficient in terms of gathering new knowledge.
 

Ronen E

Problem Solver
Staff member
Moderator
#30
WRT the death of the p-value, the 5 stages of grief and loss are: 1. Denial and isolation; 2. Anger; 3. Bargaining; 4. Depression; 5. Acceptance.
 
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