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A significance error is an error that occurs from drawing an incorrect conclusion about the level of statistical support.
The level of significant error is having a value that you are supposed to set at the beginning of your research study for assessing the statistical type probability of obtaining your results or p-value. Usually, the significance level is set at 5%. This means the idea that your results only have an occurrence of a 5% chance or less if the null type hypothesis is actually coming to be obtained as true.
When we are supposed to create an incorrect conclusion that exists within a study then what it typically means is that the study had something to proceed with a hypothesis. If you have a level of statistical support and you will definitely have errors in it as well, then the lab is incorrect.
One of the major types of common approaches that is to minimize the probability of getting a positive error in a false sense is to minimize the particular significance level of a hypothesis test. Since the level of significance is chosen by a researcher, the level can also be changed. For example, a particular significance level can be minimized to a value of 1% as all are based on levels of statistical support.
Learn to know more about the difference between a Type I significance error and a Type II significance error on
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