# 6.2 - Significance Levels

6.2 - Significance Levels

As we saw in the examples on the previous page, the consequences of Type I and Type II errors vary depending on the situation. Researchers take into account the consequences of each when they are setting their $$\alpha$$ level before data are even collected.

In many disciplines an $$\alpha$$ level of 0.05 is standard, for example in the social sciences. There are some situations when a higher or lower $$\alpha$$ level may be desirable. Pilot studies (smaller studies performed before a larger study) often use a higher $$\alpha$$ level because their purpose is to gain information about the data that may be collected in a larger study; pilot studies are not typically used to make important decisions.

Studies in which making a Type I error would be more dangerous than making a Type II error may use smaller $$\alpha$$ levels. For example, in medical research studies where making a Type I error could mean giving patients ineffective treatments, a smaller $$\alpha$$ level may be set in order to reduce the likelihood of such a negative consequence. Lower $$\alpha$$ levels mean that smaller p-values are needed to reject the null hypothesis; this makes it more difficult to reject the null hypothesis, but this also reduces the probability of committing a Type I error.

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