is an important process in research. The research showed that independent
sample t-tests were conducted to look at the differences between participants
that consumed caffeinated beverages and those who did not and they performed
worse than participants that did not drink caffeine (M= 9.81, SD= 3.16), t(97) = 2.14, p < .05. The statistical significance is important because by doing this analyzing process the researcher can make sure that their results are supporting the hypothesis that they have predicted. To be statistically significant refers to whether there are any differences between the groups being researched and if they are real or if they are showing the results the way they are based on chance (Field, 2013). Looking at the data given about the differences between participants who consumed caffeine and the ones who did not the t-test showed that the p-value was less than .05. The P-value stands for the probability of finding the observed or extreme results when the null hypothesis is true (Field, 2013). This p-value is the number that falls between 0 and 1 and if that p-value is below 0.05 then it shows that there is strong evidence against the null hypothesis which means that the researcher would reject it. When it comes to the p-value it shows significance if the p-value is at or below the 0.05, and this only has to be one of the observed results for the results to be statistically significant. The biggest factor when it comes to statistical significance with the t-tests and the p-value tell us the differences in the groups being researched. Since in research for the research to be statistically significant that means that the probability is 5% or less that the results are only because of chance, and that it means that the researcher can be 95% sure that the results are not due to chance. Statistical significance is important because this gives the researcher the likelihood that their hypothesis is correct. Though statistical significance is important, it is also important to know that it doesn't necessarily mean that the research is wrong, it is more that the hypothesis is incorrect.