Significance Level

Significance Level

       What is the correct interpretation of this sentence? “… significantly different at 0.01 or 0.05 level”.  When some people read this sentence in a research some might just ignore it as it’s too technical to understand if you are not really adept in research. My professor did an excellent job in discussing this topic to us. She clarified the topic by distinguishing general normal curve from standardized normal curve, followed by determining the value of Z-score and percentile and lastly, interpreting the meaning of the sentence mentioned above.

         In interpreting the significance level, the word significant does not necessarily mean important. It is quite misleading to the reader. The most common level, used to mean something is good enough to be believed, is .95. This means that the finding has a 95% chance of being true. In research, it will not be shown "95%" or ".95" to indicate this level. Instead "0.05," (5%) will be shown which means that the finding has a five percent (0.05) chance of not being true, which is the converse of a 95% chance of being true. If the value of "0.01" (1%) is shown it means that there is a 99% (1-.01=.99) chance of it being true.

        It must be noted that significance level is used in hypothesis testing. The lower the significance level, the more the data must diverge from the null hypothesis to be significant. Therefore, the 0.01 level is more conservative than the 0.05 level. The Greek letter alpha (α) is sometimes used to indicate the significance level.

        If the level of significance is equivalent to 0.01 (α = 0.01) or 0.05 (α = 0.05), it means that the level of confidence is equivalent to 99% or 95% respectively. 0.01 and 0.05 means the level of acceptable error. The researcher can decide which level of significance he can use.  However, 99% is more confident level than 95%. If the level of error is less, the level of confidence is conversely high.

         Furthermore, there are two kinds of error that can be made in significance testing to wit: (1) a true null hypothesis can be incorrectly rejected or the Type I error and (2) a false null hypothesis can fail to be rejected or the Type II error.