In the statistical gargon used in psychology, p refers to the probability that the difference you see between two groups (of introverts and extroverts, say, or males and females) could have occurred by chance. As a general rule, psychologists report a difference between two groups as 'significant' if the probability that it could have occurred by chance is 1 in 20, or less. The possibility of getting significant results by chance is a problem in any area of research, but it's particularly acute for sex differences research. Supppose, for example, you're a neuroscientist interested in what parts of the brain are involved in mind reading. You get fifteen participants into a scanner and ask them to guess the emotion of people in photographs. Since you have both males and females in your group, you rin a quick check to ensure that the two groups' brains respond in the same way. They do. What do you do next? Most likely, you publish your results without mentioning gender at all in your report (except to note the number of male and female participants). What you don't do is publish your findings with the title "No Sex Differences in Neural Circuitry Involved in Understanding Others' Minds." This is perfectly reasonable. After all, you weren't looking for gender difference and there were only small numbers of each sex in your study. But remember that even if males and females, overall, respond the same way on a task, five percent of studies investigating this question will throw up a "significant" difference between the sexes by chance. As Hines has explained, sex is "easily assessed, routinely evaluated, and not always reported. Because it is more interesting to find a difference than to find no difference, the 19 failures to observe a difference between men and women go unreported, whereas the 1 in 20 finding of a difference is likely to be published." This contributes to the so-called file-drawer phenomenon, whereby studies that do find sex differences get published, but those that don't languish unpublished and unseen in a researcher's file drawer. . Cordelia Fine
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It's a problem in science because, in a very real sense, it's impossible to do a randomized experiment in biology. Take two groups of mothers and one group of fathers, and ask them to take care of their newborns. Will the mothers' babies turn out better than the fathers' babies? You can't know until after both groups have had time to get used to their new babies. So the only way you can be sure that the results could not have happened by chance is to compare them to other studies done with other groups — which means that you need to find studies of other mothers and fathers who've had babies recently (try asking your local hospital and see what they can tell you), and compare them to your own.

That's difficult if not impossible: anyone who studies the biology of parenting has natural incentives to focus on women and men who've just had babies, since they're more likely to be interested in studying sex differences than other people who might be more likely to have taken part in such a study. The solution is simple: just don't use birth order as a control variable. Instead, look at all the ways that different parents may differ, and find some other way — like having access to genetic information — that can reliably distinguish between parents whose children's interests differ and those whose children's interests are similar.

Source: Delusions Of Gender: How Our Minds, Society, And Neurosexism Create Difference

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