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10-Year Cell Phone / Cancer Study Is Inconclusive
crimeandpunishment writes "A major international (retrospective) study into cell phones and cancer, which took 10 years and surveyed almost 13,000 people, is finally complete — and it's inconclusive. The lead researcher said, 'There are indications of a possible increase. We're not sure that it is correct. It could be due to bias, but the indications are sufficiently strong ... to be concerned.' The study, conducted by the World Health Organization and partially funded by the cellphone industry, looked at the possible link between cell phone use and two types of brain cancer. It will be published this week."
At least from this we know that cell phone radiation isn't causing some massive epidemic of brain cancer, and the affects, if there are any, are relatively small. That's not the biggest comfort you could have, but it's something (considering most of us are not going to give up our cell phones anyway).
Qxe4
Yeah, because surveying all those people would be ABSOLUTELY FREE and take NO TIME. Also, it's totally necessary to check everyone. Sampling and statistics don't exist.
How silly.
To get statistical significance, you don't need to sample the entire population. Beyond a certain number for a certain confidence level, you don't get very much more.
I'm a minority race. Save your vitriol for white people.
Not really. Sampling can give accurate results even when sampling a small percentage of the total population. If U.S. political polls select a sample size of between a few hundred and a thousand out of 300 million with only 3% error, it sounds reasonable that 13,000 would be a good sample size of a population 20 times that, giving the same margin of error.
Also remember that, assuming the sample is chosen well (it is a good cross-section of the population and not confined to one specific subgroup), the benefits of adding additional samples drops off. It is essentially logarithmic: at first, adding samples is a huge benefit: after a certain point, the incremental gain from one additional sample is only a tiny fraction of the first samples.
24 beers in a case, 24 hours in a day. Coincidence? I think not!
And even if there is some correlation, people need to put it in perspective.
The last time I talked to a flat-earth-er about their fear of cell phones causing cancer, they had a drink in one hand and a cigarette in the other.
Now that, Alanis Morrissette, is irony.
--- "We've always been at war with Eastasia."
I have a problem with "medical surveys" in that they a prone to make correlation-causation errors. This seems to be a measurable problem that can be tested in the lab. Why don't people do this instead. Put a lab monkey next to an active mobile phone and keep them there for several years. After that, dissect the monkey for any signs of cancer. If there is, then alert the public. You then look into how it happened, i.e the biochemical interactions that caused it. Just "surveying" people introduces biases, other factors like diet and lifestyle and also crackpots.
No, you get a smoother, more natural bass and just generally a warmer...uh, sorry, wrong thread!
The article in USA Today has a nice little gem in it: "The authors acknowledged possible inaccuracies in the survey from the fact that participants were asked to remember how much and on which ear they used their mobiles over the past decade. Results for some groups showed cellphone use actually appeared to lessen the risk of developing cancers, something the researchers described as "implausible."" Now, I don't know why, but something about this statement seems kind of important.
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The principle is correct, but you're failing to take into account the probability of an the respective events. Given that winning 60% of the vote is considered a landslide, you can think of asking someone whether they're voting Republican or Democrat as a coin flip with a small bias in one way or the other. Because the race is so close, a few extra republicans or democrats in your sample won't produce a huge error in your estimate.
On the other hand, a brain tumor can be thought of as a rare event. If the true incidence rate of brain cancer is five occurrences per thousand people over ten years, and your sample of 1,000 people has six incidences, you have a sample error of 20%. It's because of this that a small variation in the numbers can produce a large error. Therefore if you want to accurately assess the rate of cancer, you need a much bigger sample size.
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It really seems silly when, in America at least, age-adjusted rates of brain cancer have fallen or held steady since the 1990s. From the National Cancer Institute:
It would seem to me that falling cancer rates are no reason for assuming that widespread cellphone use has been a health concern.
If U.S. political polls select a sample size of between a few hundred and a thousand out of 300 million with only 3%..."
I'm not so sure those percentages are accurate.
They look accurate to me. From me undergrad stats classes, I seem to recall that to get 5% confidence level out of population of 10k, one needed a sample of around 850. For populations of 1000k, the sample size only went up by a few tens (perhaps to 900). Sampling is not linear, and it drops off the higher you go - IIRC (and I think I do), their is very little difference in the sample size for a population of 100k as there is for twenty times that number.
I'm a minority race. Save your vitriol for white people.
The uncertainty in the study is due to the low precision of their data- they asked people to try and remember how much they were typically using their cellphones. Surveying more people isn't going to get people to provide more precise data.
Also, unless the needed data is already available somewhere, gathering more data costs more money. As someone else mentioned in a sibling post, there are diminishing returns when increasing your sample size. Eventually the cost of the data will exceed the benefit to the certainty of your results.
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I'm not so sure those percentages are accurate. You'll often see different polls differ by much more than that (far more often than 5% of the time or whatever the confidence level is).
Election polling is just especially difficult, since what counts is if you actually vote and who you vote for, neither of which have been determined at the time of the poll and could change. Election polling isn't simply an opinion poll, but is obviously supposed to reflect the population of people who will actually vote on election day. The polls have differing models of selecting "likely voters", and will thus have numbers that differ more than the margin of error for any single poll. In other words, taking the margin of error for a single poll and comparing it among multiple polls is invalid, since the differing polls used different means of sample selection.
Certainly actual elections tend to fall well outside the +/- 3% accuracy claimed by many of the election-day pollsters.
I guess I haven't found that to be true if you mean "tend to" is more than 50% of the time. Sure, you're going to find some that are outside of the 3% error bars, but you'd also expect that to happen, statistically speaking.
AccountKiller
Science isn't inconclusive. There is statistically significant, or not. In this case, not.
Test another hypothesis or test again if data looks fishy.
To get statistical significance, you don't need to sample the entire population. Beyond a certain number for a certain confidence level, you don't get very much more.
Exactly right.
There was no statistical significance, which means that the cancers (or absence there of) were distributed over cell phone users and non-users (controls) with no preference for either group.
Normally this would be the end of it.
But by the way the reporter worded it (Inconclusive) and (to a lesser extent) the way the Researcher phrased it, indicates a clear predilection toward finding a positive correlation, which they could not do.
The takeaway is not that the study "inconclusive". The scientific takeaway is that there is yet again no evidence of correlation between cancer and cell usage.
Its over. The absence of evidence destroys this theory. Time to move on.
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Here are some additional details for those of you so inclined.
Consider a simple binary choice question. This is easily modelled by the binomial distribution which has well understood distributions. (Other distrbutions may be relevant but the principles remain pretty constant across them all.) The standard deviation is given by sqrt[np(1-p)] where n is the sample size and p is the probability of the observation you are interested in (the mean is np so in what follows I will be dividing by n to talk about percentages if you are taking notes). For example, are you male? If the true p is, say, 75% then you need a sample size of approximately 833 to get a 95% confidence interval (2 s.d.) of +/- 3%.
You might also note that the closer the true p is to 50%, the larger the sample size needed. If the true p is 50% you need a sample size of approximately 1100 for the same confidence interval. Furthermore, if you want to get it within 1%, the sample size goes up dramatically - to 10,000.
The population size is pretty much irrelevant. The population matters for ensuring that your sampling is truly random, but political pollsters can use the same sample sizes in Australia (pop ~20 million) as in the US (pop ~300 million) for similar accuracy. (Sampling bias is the reason that political polls can be out by so much - if you call households during work hours you are going to get a very different sample of people than if you call at dinner time.)