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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."
I'd very much like to know how they "surveyed" the people. Simply asking people if they've experienced any effects from cell-radiation is almost bound to get "yes" answers from flat-earth-society-wannabes. I'm guessing that the survey was a survey, and not a blind study (i.e. the subjects aren't aware of the correlation being investigated when questioned).
I'm a minority race. Save your vitriol for white people.
I can't wait for Watts Up With That to release a few pictures of some of the survey participants walking out of tanning salons and living near radar towers and claiming the study is a sham as a result. Besides, everyone already knows that scientists keep doing these studies just because they're greedy for all the grant money they get rich from.
What a fool believes, he sees, no wise man has the power to reason away.
Inconclusive != negative result. There's a big difference.
Besides, everyone already knows that scientists keep doing these studies just because they're greedy for all the grant money they get rich from.
I'm amazed that the population in general have taken so long to realise that science has become a huge scam; I'd figured that out when I was studying for my Physics degree back in the 80s.
Science isn't inconclusive. There is statistically significant, or not. In this case, not.
Test another hypothesis or test again if data looks fishy.
Getting a conclusive answer isn't hard, the problem is figuring out what questions you can apply that answer to.
Lets look at a hypothetical study looking at car colour and accidents. Trying to decide what colour of car you should buy to avoid getting in an accident.
The researchers take a bunch of police reports, count up the numbers for each colour involved, compare that to car sales, and find that red cars are over-represented by 15%.
Did that study show that drivers of red cars are more likely to get in accidents? Not necessarily, maybe the red car drivers like their cars more, keep them in better shape, and thus those cars stay in the driving population longer. Thus red is overrepresented by 15% in the total number of cars.
Say you have another study that eliminates that possibility. So can you now say that red cars make people worse drivers? Well maybe people who like driving buy red cars, so they drive 15% more than other drivers and get in 15% more accidents.
Ok, control for that. Maybe the red makes the drivers more aggressive? Then again maybe people who are already aggressive simply buy red cars.
Or maybe it's not the colour but something in certain red paints causes drivers to act irrationally.
You can go on like this for a very long time, studies like this are powerful, but they're also tricky because if you want a useful answer you have to interpret your data and decide if there's important aspects you overlooked.
If you want to know if the red paint causes accidents seeing who buys the cars is probably a pretty important question to ask. Testing the paints for hallucinogens, well you can probably safely ignore that possible bias.
I stole this Sig