I have seen many cases where type I errors are accounted for (denoted by an alpha value) in various research articles. I have found it rare that a researcher will take into consideration the power, or the type II error.
Type II errors can be a big deal right? We have accidentally rejected the alternative hypothesis when it was actually false. Why are alpha values emphasized so much instead of beta values?
When I took first year statistics, I never was taught beta—only alpha. I feel that these two errors should be treated equally. Yet, only alpha seems to be emphasized.