Stat Abuse the Precautionary Principle Term Paper

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Despite some evidence that genetically modified crops might be harmful to people and the environment -- something Saunders thinks falls under the umbrella of the precautionary principle -- companies and some governments are claiming that they have "proven the crops are safe" when in fact all they have done is failed to prove that it is unsafe. A study that doesn't show a statistically significant correlation between genetically modified foods and specific harms to people/the environment is not proof the genetically modified foods are safe, that is, it simply doesn't show them to be unsafe. While failing to show a statistically significant correlation is sufficient grounds to assume there is no correlation in some statistical applications (assuming the study was carried out properly, of course), in cases where there is potential harm and the precautionary principle applies, the exact opposite should be the case -- the correlation should be assumed to exist until it can be positively shown not to. Saunders example of a coin is the simplest way to understand this: if you're worried that a coin might be biased towards heads and you flip it three times in a row and get a heads each time, you haven't proven that the coin is biased as the p-value is still 0.125, but you certainly haven't proven that the coin is unbiased just because the p-value is well within the realm of possibility.

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