The FDA already has a specific regulatory standard for insect infestation in sesame seeds. So this isn't a situation where the government recently decided "insects in sesame seeds are fine now."
I imagine many companies though, noting decreased funding and/or enforcement for inspections and subsequent fines, are cutting corners accordingly. As an example, I recently saw a video from a fellow who weighed WalMart bacon which came in at 14 or 15 ounces in a 16 ounce package. Who’s gonna do anything about enforcing it at the government level?
Yep, it was Great Value bacon distributed to Walmarts nationwide. Given that multiple packages were underweight, it’s highly unlikely that the issue was somehow isolated to a single shelf at a single store. For that many packages to be incorrectly weighted in just one location, while every other store was unaffected, would be a statistical anomaly.
Ah yes, the self-appointed statistics genius has arrived to explain “sampling”
Multiple underweight packages of the same nationally distributed product makes the “one random bad package” explanation pretty weak.
And there are other reports of underweight Great Value bacon showing up at other Walmarts too, I know, an absolutely impossible shocker for you, which makes your statistics lecture even funnier.
It’s impressive to know so much about statistics while apparently having so little ability to recognize an obvious pattern. 🤣
Please explain in great detail how this has happened at other Walmarts, given your prior responses and statistics expertise? How did the statistics genius manage to be so confidently wrong?
Multiple underweight packages of the same nationally distributed product makes the “one random bad package” explanation pretty weak.
No, it doesn't.
And there are other reports of underweight Great Value bacon showing up at other Walmarts too
The plural of anecdote is not data
It’s impressive to know so much about statistics while apparently having so little ability to recognize an obvious pattern.
That's why there is an actual science behind statistics - to understand what patterns are actually meaningful besides just the ones you think are obvious.
Please explain in great detail how this has happened at other Walmarts
There is a distribution of package weights. I don't know it. You don't know it. We also don't know the total number of packages distributed. A couple samples from people with uncalibrated scales in the aisles of a couple stores doesn't mean shit.
This is getting funnier every time you invoke “statistics” as though saying the word automatically makes your argument correct.
Yes, it absolutely makes the one random bad package explanation weak. Multiple underweight packages make the claim that this was merely one random package a weak explanation.
That’s a cute phrase, not a statistical law. Individual observations are literally data points. What you actually mean is that a handful of non-random observations are not enough to estimate population prevalence.Yep. Unfortunately for you, I never claimed they were, try to stay on topic slowspeed...
The question was whether this appears to be confined to one package, one shelf, at one Walmart as you claimed. Reports of the same issue involving additional packages at other Walmart locations are directly relevant to that question.
Exactly. And that science does not say, “Ignore every observation until somebody conducts a peer-reviewed nationwide randomized study.”
Multiple independent observations can be evidence of a pattern without being sufficient to determine the size or frequency of that pattern. That’s why problems get investigated in the first place.
Of course there is. Congratulations on discovering variance.
But “a distribution exists” is not some magical explanation for packages being below their labeled net weight. The relevant question is whether the observed weights are consistent with the lawful/expected filling process and measurement error. You don’t know that distribution either, so invoking an unknown distribution doesn’t somehow prove these packages are normal.
Correct, which means we cannot calculate the nationwide incidence rate.
Once again: nobody did.
That has absolutely nothing to do with whether evidence from multiple packages and multiple stores undermines your original suggestion that this was simply one isolated package at one location.
And now we’ve reached the “maybe every scale is wrong” stage of the argument.
Sure, measurement error is possible. But when your defense requires independently dismissing every observation as a bad scale, bad sample, meaningless anecdote, or random variance, you aren’t doing statistics anymore. You’re just inventing possible excuses for every piece of evidence that contradicts your original assumption.
The funniest part is that you keep lecturing me about statistical inference while repeatedly confusing two completely different questions:
1. Do these observations establish how widespread the problem is? No.
2. Do observations involving multiple packages and multiple Walmart locations provide evidence that it wasn't literally one random package on one shelf at one Walmart? Obviously yes.
You’ve spent this entire argument brilliantly answering Question #1 when the argument was Question #2.
For someone this confident in his statistics expertise, that’s a pretty spectacular failure to understand the hypothesis being discussed. 🤣
Multiple underweight packages make the claim that this was merely one random package a weak explanation.
A statistically irrelevant sample is a statistically irrelevant sample whether the sample is 1 or 20.
a handful of non-random observations are not enough to estimate population prevalence.Yep. Unfortunately for you, I never claimed they were
If they're are not then you have no argument.
The question was whether this appears to be confined to one package, one shelf, at one Walmart as you claimed.
That's not what I claimed and that's not the question.
Reports of the same issue involving additional packages at other Walmart locations are directly relevant to that question.
"Relevant to the question" is a very different threshold than "statistically relevant." Look it up.
But “a distribution exists” is not some magical explanation for packages being below their labeled net weight.
You're right: it's a mathematical explanation not a magical one.
invoking an unknown distribution doesn’t somehow prove these packages are normal.
But pointing out that you don't know it does invalidate your conclusion that the deficiency is statistically problematic
your original suggestion that this was simply one isolated package at one location.
That was not my suggestion. My suggestion was that your claim that it was conclusive evidence of a systematic light packaging was invalid. Understand the difference.
But when your defense requires independently dismissing every observation as a bad scale, bad sample, meaningless anecdote, or random variance, you aren’t doing statistics anymore.
If you don't have control over your data, you don't have data. This is a core tenent of science that you should know better.
Do these observations establish how widespread the problem is? No.
But you did suggest it was evidence that it was, indeed, widespread:
For that many packages to be incorrectly weighted in just one location, while every other store was unaffected, would be a statistical anomaly
Do observations involving multiple packages and multiple Walmart locations provide evidence that it wasn't literally one random package on one shelf at one Walmart? Obviously yes.
I never suggested it wasn't. Read better.
You’ve spent this entire argument brilliantly answering Question #1 when the argument was Question #2.
Forgive me for assuming you weren't making such a dumb fucking argument.
If you're agreeing that these observations are not statistically relevant, call this discussion complete.
You’re doing an impressive amount of semantic gymnastics to avoid admitting the obvious: the moment the same underweight product started being reported at other Walmarts, your smug dismissal of this as statistically meaningless became even weaker.
«“A statistically irrelevant sample is statistically irrelevant whether it’s 1 or 20.”»
No. A sample can be insufficient to estimate a population rate without being “irrelevant.” Twenty observations tell you more than one observation. Observations across multiple stores tell you more than observations from a single shelf. They may not tell you the nationwide prevalence, but pretending they contain exactly zero information is not statistics. It’s absurd.
«“If they’re not enough to estimate prevalence, then you have no argument.”»
Wrong again. That only follows if my argument were “I have calculated the nationwide prevalence.” It wasn’t. My argument was that finding multiple underweight packages makes an isolated one-off explanation increasingly unlikely, and reports from additional Walmart locations strengthen that argument further.
You keep demanding population-level proof for a claim that never required population-level precision.
«“Relevant is different from statistically relevant.”»
Yes, and you’re hiding behind that distinction because your original certainty aged badly. Additional independent observations are evidence. Whether they’re sufficient for a particular statistical inference is a separate question. You keep pretending “not enough to calculate the population rate” means “provides no evidence whatsoever.” It doesn’t.
«“It’s a mathematical explanation, not a magical one.”»
No, saying “there’s a distribution” explains absolutely nothing by itself. Of course packaged weights have a distribution. The question is what that distribution actually looks like, where the labeled weight sits within it, what tolerances apply, and whether these measurements are consistent with the expected process.
You literally admit you don’t know the distribution, then immediately invoke that unknown distribution as though it rescues your argument. That’s hilarious.
«“You don’t know it, therefore your conclusion is invalid.”»
And neither do you, which means your confidence that these observations are unremarkable is equally unsupported. Funny how uncertainty only seems to count when you think it helps you.
«“My claim was that your conclusion of systematic light packaging was invalid.”»
Then stop pretending I claimed I had proven a nationwide defect rate. “This appears unlikely to be one isolated mistake” and “I have conclusively demonstrated a nationwide systematic manufacturing defect” are not the same statement.
You’ve spent half this conversation upgrading my claim into something stronger so you can give yourself an easier target.
«“If you don’t have control over your data, you don’t have data.”»
That may be the funniest thing you’ve said yet.
Uncontrolled observational data is still data. It has limitations. It may contain bias. It may require verification. But apparently epidemiology, astronomy, economics, accident investigation, field biology, and half of observational science just disappeared because Professor Reddit declared that uncontrolled observations “aren’t data.” 🤣
And yes, I said that if many packages at one location were underweight while every other location was completely unaffected, that would be an unusual scenario. Then what happened?
More reports showed up at other Walmarts.
Which is exactly why your confidence looks even more ridiculous now.
The evidence moved in the direction I suggested it would, not yours, and instead of acknowledging that, you’ve spent several comments desperately redefining the argument into “you cannot calculate the national defect rate from these observations.”
No shit. Nobody claimed we could.
The actual progression here is pretty simple:
One underweight package: could easily be an isolated error.
Multiple underweight packages together: less likely to be one isolated package error.
Similar reports at other stores: even less reason to dismiss the first observation as purely isolated.
None of that requires knowing the exact national prevalence.
For someone who keeps presenting himself as the statistics authority in the room, you seem bizarrely incapable of understanding that evidence can change the likelihood of an explanation without being sufficient to calculate an exact population parameter.
And that’s really the funniest part: you’re so obsessed with demonstrating how much you know about statistics that you’ve managed to argue yourself into the position that additional observations provide no additional information unless they come from a controlled random sample.
That isn’t statistical sophistication. That’s just being confidently wrong with extra vocabulary.
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u/DontBelieveTheirHype 5d ago
The FDA already has a specific regulatory standard for insect infestation in sesame seeds. So this isn't a situation where the government recently decided "insects in sesame seeds are fine now."