The significance level differs among industries depending on their individual guidelines and specifications. One might not require the same amount as the other thus the need for accurate testing and results. Sticking to the specifications is important for the safety of everyone. Rules are made so that people are not hurt and if they are they will be compensated accordingly. I believe that the null hypothesis would be more likely to be rejected at .10 than .01. By definition the type I error occurs when researchers reject a true hypothesis. The more it increases the more unlikely it is within the guidelines and will be rejected. As it increases it becomes a type II error. In order to reduce the likelihood of incurring this error the researcher should periodically do sampling or quality control to avoid this happening.
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