Hypothesis testing is the art of testing if variation between two sample distributions can just be explained through random chance or not. It is based on probabilities so there is always a chance of making an incorrect conclusion. There are possibilities for having two types of error: type I and type II. The risks of these two errors are inversely related and determined by the level of significance and the power for the test. Therefore, you should determine which error has more severe consequences for your research work before you gauge their risks. Researchers have to know that no hypothesis testing is 100% certain. The probability of making a type I error is α. An α of 0.05 indicates that the researcher is willing to accept a 5% chance that results are wrong when the null hypothesis is rejected. The researcher has to carefully determine the consequences of making one type of error that could result in more severe loss than other type of error.
Key words: Hypothesis, Type I and Type II errors, Statistics,Hypothesis
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