![]() ![]() The larger the actual difference between the groups (ie. The sample size or the number of participants in your study has an enormous influence on whether or not your results are significant. Of course, this would impose a stricter criterion and if found significant we would conclude there is a less than 1% chance the null hypothesis is correct. 1%) in order to consider our results significant. If we set our alpha to 0.01, we would need our resulting p-value is be equal to or less than 0.01 (ie. The p-value only tells us whether or not the groups are different from each other, we need to make the inferential leap assume teaching styles influenced the groups to be different.Īnother way of looking at a significant p-value is to consider the probability that if we run this experiment 100 times, we could expect at least 5 times the student test scores to be very similar to each other. Notice we didn’t say the different teaching styles caused the significant differences in student test scores. If this is the case, we reject the null hypothesis, accept our alternative hypothesis, and determine the student test scores are significantly different from each other. less than 0.05) would indicate that there is a less than 5% chance that your null hypothesis is correct. Obtaining a significant result simply means the p-value obtained by your statistical test was equal to or less than your alpha, which in most cases is 0.05.Ī p-value of 0.05 is a common standard used in many areas of research.Ī significant p-value (ie. ![]() The p-value (also known as Alpha) is the probability that our Null Hypothesis is true. Now that we have set the stage let’s define what is a p-value and what it means for your results to be significant. In our teaching style example, the null hypothesis would predict no differences between student test scores based on teaching styles.Īlternative or Research Hypothesis: Our original hypothesis which predicts the authoritative teaching style will produce the highest average student test scores. Null Hypothesis: Assumed hypothesis which states there are no significant differences between groups. unmeasured) variable? Last but not least, is 8% considered “high enough” to be that different from 80%? However, what if we ran this experiment 100 times, each time with different groups of students do you think we would obtain similar results? What is the likelihood that this effect of teaching style on student test scores occurred by chance or another latent (ie. It would seem your hypothesis was correct, the students taught by the authoritative teacher scored on average 8% higher on their tests compared to the students taught by the authoritarian teacher. Let’s assume the average test score for the authoritarian classroom was 80%, and the authoritative classroom was 88%. At the end of the year, we average all the scores to produce a grand average for each classroom. Throughout the semester, we collect all the test scores among all the classrooms. One classroom is taught by an authoritarian teacher and one taught by an authoritative teacher. In order to accurately test this hypothesis, we randomly select 2 groups of students that get randomly placed into one of two classrooms. If you would like to learn more about the various research design types visit my article ( LINK ).įor example, we want to test a hypothesis that an authoritative teaching style will produce higher test scores in students. independent variables) produce a change in another variable (ie. ![]() Specifically, we hypothesize that one or more variables (ie. A true experiment is used to test a specific hypothesis(s) we have regarding the causal relationship between one or many variables. We’ll discuss significance in the context of true experiments as it is the most relevant and easily understood. Significance (p = 0.05)įirst and foremost, let’s discuss statistical significance as it forms the cornerstone of inferential statistics. I want to take this time and discuss statistical significance, sample size, statistical power, and effect size, all of which have an enormous impact on how we interpret our results. Obtaining significant results is a tremendous accomplishment in itself self but it does not tell the entire story behind your results. I guess all you have left to do is write up your discussion and submit your results to a scholarly journal. Photo by Aleksandar Cvetanovic on UnsplashĬongratulations, your experiment has yielded significant results! You can be sure (well, 95% sure) that the independent variable influenced your dependent variable. ![]()
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