Neuroscientists perform brain scans by dividing the scans into thousands of small pieces called voxels, which correspond to regions in the brain. When a brain is scanned (even a dead brain), theres always some random “noise” in each voxel. As there are thousands of voxels, the odds of one producing data that corresponds to the stimulus given are actually quite high. In more serious studies, however, its not always clear if observational data arises by chance. Scientists thus use probability theory to address this issue. Let’s say you are a scientist, testing a new drug to see if it cures a certain illness. A mathematical tool for problems like this is called the null hypothesis significance test. First, you start with your null hypothesis, which is an assumption of what will happen in your test. In this case, your null hypothesis is that the new drug does nothing at all. Next, you take a look at the deviation of the data you observed in the experiment. You need to consider the probability that your data came about by chance – this is called the p-value. If the probability is less than a certain p-value (usually p = 0.05), the data is considered statistically significant. And if your data is statistically significant, that means you know with 95 percent certainty that the new drug has the proposed effect. How Not to Be Wrong by Jordan Ellenberg
Posted on: Sat, 03 Jan 2015 20:52:52 +0000
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