Posts written by Thomas Lumley (2569)

• The “Great Kiwi Christmas Survey” led to stories at Herald, Newshub, Farmers Weekly, and Radio NZ on what people were eating for their Christmas meal.  The respondents for the “Great Kiwi Christmas Survey” were variously described as “over 1000”, “over 1800”, and “over 3300” Kiwis, which seems a bit vague. According to newsroom, this was actually a bogus poll: “We promoted the survey through social media channels and sent the survey to those people who had signed up to receive information from us,” concedes Lisa Moloney, the promotions manager for Retail Meat NZ and Beef + Lamb NZ.  Headlines based on bogus polls aren’t ever ok — even when you don’t think the facts really matter. Newsroom argued that the results under-represented vegetarians, which is plausible, but you can’t really tell from the data presented on the number of vegetarians. Not all Christmas meals at which vegetarians are present will be centred around plant-based food, as any vegetarian can tell you.
• Stuff, with the help of Auckland Transport, wrote about Auckland’s most prolific public transport user. Apparently, someone took 3400 trips over a year.  It’s surprising that’s even possible: nearly ten trips per day, every day,  and since the person is doing this on a gold card, starting no earlier than 9am on weekdays.  Assuming the numbers are correct — actually, whether the numbers are correct or not — it’s also a bit disturbing that this analysis was done.  The summaries of typical and top 100 users seem a lot more reasonable. The piece says “Stuff asked to interview the person, however Auckland Transport would not reveal their identity for privacy reasons.”, which is good, but you might want them not to be in a position to reveal it.
• “Support for low-income housing followed a similar pattern, with broad approval for building it someplace in the country (82 percent) but much less for building it locally (65 percent)” at 538. There should be a word for this.
• Interesting discussion on the Slate Money podcast about a data display, the “Fed Dot Plot”, which shows the best guesses of members of the Federal Reserve Open Market Committee as to what interest rates they will want in the future; each dot is one person.  The Fed is trying to de-emphasise this graph at the moment — partly because people tend to over-interpret it. Importantly, there’s no individual uncertainty shown, and there’s no way to tell how much of the difference between people is due to difference in what they think the economic situation will be and how much is due to differences in how they expect they will want to react to it.
  

The BBC: Hershey sued in US over metal in dark chocolate claim. 

This is a slight variation on normal headline grammar:  Hershey isn’t being sued over something they claimed; they are being sued because Consumer Reports claims to have found surprisingly high concentrations of lead and cadmium in dark chocolate from a wide range of manufacturers, small and large, organic and conventional, fair-trade and … whatever the opposite of that is.  The cadmium seems to come from the soil — chocolate eaters are on the wrong end of phytoremediation here — and the experts don’t actually know where the lead comes from. Hershey is being sued because they’re a potentially rewarding target, not because they are more at fault than other chocolate makers.

So, how bad is it? Consumer Reports say that the heavy-metal concentrations exceed health standards if you eat an ounce (like, 30g) every day. To get this result, they used the strictest health thresholds they could find: as they phrase it, “CR’s scientists believe that California’s levels are the most protective available”.  We can look at how California computed its threshold (MADL) for cadmium — at least, how it did in 2001; it’s possible there’s a stricter threshold that I haven’t found on Google.  The procedure was to take the highest concentration with no observed adverse effects in animals, scale it by weight, and divide by 1000 for safety.  With cadmium, they didn’t have a no-effect study, they only had a study showing adverse effects, so they put in an extra factor of 10 to account for that.  So, the threshold we’re comparing to is 10,000 times lower than the lowest concentration definitely shown to be harmful.  The California law doesn’t say it’s dangerous to exceed this threshold; it says that if you’re under this threshold you’re so safe that you don’t have to warn consumers that there’s cadmium present. (PDF)

For chemicals known to the state to cause reproductive toxicity, an exemption from the warning requirement is provided by the Act when a person in the course of doing business is able to demonstrate that an exposure for which the person is responsible will have no observable reproductive effect, assuming exposure at 1,000 times the level in question

Presumably the same is basically true of lead.  Now, lead and cadmium are well worth avoiding, even at levels not specifically known to be harmful. Lead, in particular, seems to have small adverse effects even at very low concentrations.  But the level of risk from doses anywhere in the vicinity the California MADL is, by careful design, very low.

We can look at NZ dietary exposures to cadmium, in the incredibly-detailed NZ Total Diet Study (PDF). We’re averaging about 5.2 ug per kg of bodyweight per month for women, 6.6 for men, and 12 for 5-6year old kids. The provisional monthly tolerable dose given in that report is 25.

Our numbers are a bit  higher than France and Australia, a bit lower than Hong Kong, and about the same as Italy.  If you take the hypothetical 58kg woman used in the California regulatory maths, she would consume about 10 ug/day of cadmium. The California limit is 4.1 and the NZ limit is 48. So, an ounce of high-cadmium dark chocolate per day, if it’s, say, twice the California limit, is a significant fraction of the typical cadmium consumption, but well under any levels actually known to have health risks.

For years, the StatsChat rule on dark chocolate has been “If you’re eating it primarily for the health benefits, you’re doing it wrong”. That still seems to hold.

Stuff has a new fact-check column, “The Whole Truth”, and there’s a good example with discussion of youth crime trends, by James Halpin.

The graphs are just the sort of thing I use and recommend: enough history and (where appropriate) enough context to see what trends are just continuing and where things might have changed

It’s clear that the orange line in the left panel is different from basically everything else.  It looks as though the blue line might be going up, but it’s clearly still lower than it was in recent years.

That is, one category of crime in one age group is up.  Overall, robberies and burglaries, even those specifically committed by young people, aren’t increasing, but these vehicle crimes are.  They go on to say that ram-raids by young people are up; the absolute numbers are small, but these are serious crimes, with damage out of proportion to the amount stolen. It’s unlikely to be reporting bias — again, these are serious crimes that would usually be reported.

The data can’t really support a general ‘kids today’ narrative, but there is a real, specific, problem.

• I’ve often complained about misleading bar graphs in reporting electoral opinion polls. 1News just punted on the whole issue with this:
  
• The cost of the Meola Road rebuild, $47.5 million, has been inaccurately portrayed as the cost of the bike lane that’s a minor component of it. Twitter user @ArcCyclist got the actual breakdown from the Council:
  
  While I’m at it, I do want to note one way it’s a bad table: the cycleway number is given to whole dollars, with everything else given in cents, so it looks even smaller than it really is. You usually don’t want to delete trailing zeroes in a table.
• The ESR Covid wastewater dashboard is now at poops.nz. Yes, really.
• There’s a new “technical report for future UK Chief Medical Officers, Government Chief Scientific Advisers, National Medical Directors and public health leaders in a pandemic” from the UK. Even if you aren’t among that exalted company, some of the information may be useful to public citizens as well
• The Ministry of Health is seeking public comment on something it wrote about ‘precision health’. There might be StatsChat readers who have reckons.
• Eric Crampton notes that cost-benefit ratios for transport projects are defined in an idiosyncratic way that makes them hard to compare either with each other or with non-transport projects.
• The first drug to convincingly delay Type I diabetes onset has been approved. The average benefit is about two years, and the treatment will be marketed at US$200,000.  Cost-effectiveness research suggests this is way more than it’s worth for most people, even in the US where insulin for Type I diabetes is very expensive.

From the Guardian: Computer says there is a 80.58% probability painting is a real Renoir. The story goes on to say Dr Carina Popovici, Art Recognition’s CEO, believes that this ability to put a number on the degree of uncertainty is important.

It’s definitely valuable to put a number on the degree of uncertainty. What’s much less clear is that it’s valuable to put a number on the uncertainty to four-digit precision.   Let’s think about what it would take to be that precise.

If the 80.58% number was estimated from a proportion of observed data in some sense, quoting it to four digits would only make sense if the uncertainty was less than about 0.05%.  A standard error of 0.05% would need a sample size of more than five hundred million.

Another way you can get an estimate with high precision is including subjective expert opinion, which would be entirely appropriate in a context like this. There’s no limit to how precise this can be for the person whose opinion it is — you believe exactly what you believe — but there are very strong limits on how precise it can realistically be as a guide to others.  If the computer isn’t the one buying the Renoir, other people probably shouldn’t care about its opinion to more than one or two digits accuracy.

Sometimes when you come up with an estimate you want to quote it to higher precision than is directly useful — lots of statistical software, including some I write, quotes four or more digits in the default output. This allows rounding to happen closer to the point of use, such as before it’s in a headline in the mainstream media.

From Hannah Martin at Stuff: Only 35% of Covid cases being reported, ministry says, after earlier saying it was 75%

The ministry’s latest Trends and Insights report, released on Monday, said “approximately three quarters of infections are being reported as cases”, based on wastewater testing.

However, it has since said that “based on updated wastewater methodology”, about 35% of infections were reported as cases as of the week to November 2.

This is a straightforward loss during communication:  the 75% was an estimate of how much the reporting had changed since the first Omicron peak, but it got into the Trends and Insights report as an absolute rate.  Dion O’Neale is quoted further down the story explaining this.

For future reference, it’s worth looking at what we can and can’t estimate well from various sources of information we might have.

The wastewater data has the advantage of including everyone in a set of cities and towns, adding up to the majority of the country; everybody poops. It has the disadvantage of not directly measuring cases or infections.  The wastewater data tells us how many Covid viral fragments are in the wastewater.  How that relates to infections depends on how many viral fragments each person sheds and how many of these make it intact to the collection point.  This isn’t known — it’s probably different for different people, and might depend on vaccination and previous infections and which variant you have and age and who knows what else. However, the population average probably changes slowly over time, so if the number of viral fragments is going up this week, the number of active infections is probably going up, and  if the number of viral fragments is going down, the number of active infections is probably going down.

Using the wastewater data, we can see that the ratio of reported cases to wastewater viral fragments has been going down slowly since the first Omicron peak.  We’ve got a lot of other reasons to think testing and reporting is going down, so that’s a good explanation. It’s especially a good explanation because most of the other reasons for a change (eg, less viral shedding in second infections) would make the ratio go up instead.  So, with the ratio of reported cases to viral fragments going down by 25% it makes sense to estimate that the ratio of reported cases to infections has gone down 25%.

Now all we need is to know what the reporting rate was at the peak. Which we don’t know. It couldn’t have been much higher than 60%, because some infections won’t have been symptomatic and some tests will have been false negatives.  If it was 60%, it’s down to roughly 40% now.  If it was lower than that at the peak, it’s lower than 40% now.  You could likely get somewhat better guesses by combining the epidemic models and the wastewater data, but it’s always going to be difficult.

You might think that hospitalisation and death data are less subject to under-reporting. This is true, but the proportion of infections leading to hospitalisation is (happily) going down due to vaccination and prior infection, and the proportion leading to death is (happily) going down even more due to better treatment.  On top of those changes, hospitalisation and death lag infection by quite a long time. The hospitalisation rate and death rate are directly important for policy, but they aren’t good indicators of current infections.

So, we’re a bit stuck. We can detect increases or decreases in infections fairly reliably with wastewater data, but absolute numbers are hard.  This is even more true for other diseases — in the future, there will hopefully be wastewater monitoring for influenza and maybe RSV, where we expect the case reporting rate to be massively lower than it is for Covid.

To get good absolute numbers we need a measurement of the actual infection rate in a random sample of people. That’s planned — originally for July 2022, but the timetable keeps slipping. A prevalence survey is a valuable complement to the wastewater data; it gives absolute numbers that can be used to calibrate the more precise and geographically detailed relative numbers from the wastewater.  Until we have a prevalence survey, the ESR dashboard is a good way to get a feeling for whether Covid infections are going up or down, and how fast.

Some years ago, at the behest of Kiwi Nerd Twitter, I looked at whether the Roy Morgan poll results varied more than those from other organisations, and concluded that they didn’t. It was just that Roy Morgan published polls more often. They had a larger number of surprising results because they had a larger number of results.  Kiwi Nerd Twitter has come back, asking for a repeat.

I’m going to do analyses of two ways of measuring weirdness, for the major and semi-major parties. All the data comes from Wikipedia’s “Opinion polling for the next NZ Election“, so it runs from the last election to now.  First, I’ll look at National.

The first analysis is to look at departures from the general trend.  The general trend for National (from a spline smoother, fitted in R’s mgcv package, in a model that also has organisation effects) looks like this:

Support was low; it went up.

I subtracted off the trend, and scaled the departures by the margin of error (not the maximum margin of error). Here they are, split up by polling organisation

The other analysis I did was to look at poll-to-poll changes, without any modelling of trend. The units for these are just percentage points.

Next, the same things for Green Party support: departures from their overall trend

And poll-to-poll differences

For ACT:

And finally for Labour

So, it’s complicated. The differences are mostly not huge, but for the Greens and Labour there does seem to be more variability in the Roy Morgan results. For National there isn’t, and probably not for ACT.  The Curia polls are also more variable for Green but not for Labour.  I think this makes Roy Morgan less weird than people usually say, but there does seem to be something there.

As an additional note, the trend models also confirm that the variance of poll results is about twice what you’d expect from a simple sampling model. This means the margin of error will be about 1.4 times what the pollers traditionally claim: about 4.5% near 50% and about 1% near the MMP threshold of 5%

An ongoing theme of StatsChat is that single political polls aren’t a great source of information, and that you need to combine them. A case in point: this piece at Stuff describing a new Horizon poll.  The headline is Winston Peters returns to kingmaker position in new political poll, and the poll has NZ First on 6.75%.  My second-favourite NZ poll aggregator, Wikipedia, shows other recent polls, where the public results from Curia, Roy Morgan, and Kantar were 2.1%, 1%, and 3% and a leaked result from Talbot Mills was 4%.  It’s possible that this shows a real and massive jump over the past couple of weeks. Stranger things do happen in politics — but not much stranger and not all that often. It’s quite likely that it’s just some sort of blip and doesn’t mean much.

Stuff does add “The poll had a margin of error of 3.2%, meaning NZ First’s crossing the 5% threshold was within the margin of error,”  but that’s the wrong caveat.   The 3.2% margin of error is more strictly called the ‘maximum margin of error’, because it’s the margin of error for proportions near 50%, which is larger than at, say, 5%.  I’ve written before about calculating the corresponding margin of error for minor parties.

In this case, under the pure mathematical sampling approximations used to get 3.2%, a 95% uncertainty interval for NZ First’s true support would go from 5.2% to 8.5%. If we only worried about sampling error, NZ First would be fairly clearly above the 5% threshold.  The problem is that the mathematical sampling error  is typically an underestimate of total survey error — and when you get a very surprising result, it’s sensible to consider that you might possibly be out on the fringes of the total survey error.  Or not. We will find out soon.

The friendly StatsChat busbots have been tracking cancellations as well as delays: for the past month in Auckland and longer in Wellington.  Here’s a summary of the Auckland cancellations

They seem to be up again, which might not be entirely unconnected with the current increase in Covid cases. Perhaps more to the point, that’s a lot of missing buses.

This story is a month old now, but Stuff served it up to me again, and I didn’t write about it earlier. The headline is Workers are discovering the ‘Great Regret’ of quitting their jobs, and the key data-based line is

In the United States, a survey of more than 15,000 workers by job-search platform Joblist found 26 percent of workers who quit during the Great Resignation regret their decision.

I’ve edited that quote so that the link works, which it doesn’t on Stuff or newsroom. If you follow the link, you find that is basically what Joblist says in its writeup of the survey.  If you are naturally suspicious or nerdy enough to read the Methodology section at the bottom of the page, though, it looks a bit different

We surveyed 628 job seekers who quit their previous job about why they quit and whether they regret their decision.

So, the 26% is of 628 people, not more than 15,000.  More specifically, it’s 628 job-seekers. The target population doesn’t appear to include people who already had a new job or who weren’t looking for one — two groups that you’d expect to have fewer regrets.

The Methodology section doesn’t say how they chose the people to survey, though it does say This data has not been weighted, and it comes with some limitations. At best, that suggests a survey with no attempt to compensate for non-response. At worst, it could be a bogus self-selected straw poll.