Rejecting the null hypothesis
One of the most challenging things for students to understand is what it means to reject the null hypothesis. How is rejecting the null hypothesis related to statistical significance? Please use your own words (very important!) to describe what information you use to determine statistical significance, and give an example of this information (for example, how might you check to see if your oneway ANOVA is significant? How about a Two-Way ANOVA?). Once you decide that an effect is significant, for example, please indicate what that means to you IN YOUR OWN WORDS. The point of this discussion is to help you help each other learn, not to copy down a perfect answer. Finally, do you see any problems with/limitations of NHST? Why or why not?
Sample Solution
When determining statistical significance, the null hypothesis is rejected in favor of an alternative hypothesis if the test statistic falls outside a predetermined critical region (Gigerenzer et al., 1989). In other words, when assessing data from experiments – researchers need to determine whether or not their results are due chance alone or actually represent meaningful information. This is done by calculating probability levels which enable investigators can determine how likely it is for certain outcomes to occur given a specific set of conditions.
For example – when running a one-way ANOVA you would use an F-test statistic to compare different means across multiple groups in order see if any significant differences exist between them (McDonald, 2014). If the calculated value is higher than your pre-determined level based on degrees of freedom and sample size then you can conclude that there are statistically significant differences among your groups.
In regards to two-way ANOVA – this type of analysis goes beyond simple comparisons between separate variables in order assess interactions as well (Darling & McLean, 2015). Thus – instead of only testing for overall main effects with one factor – it requires researchers account for relationships involving several factors at once before making conclusions about whether results are valid or merely due random chance.
Overall – understanding the concept behind rejecting the null hypothesis helps researchers draw reliable inferences from data by enabling them make decisions regarding what evidence supports their theories versus what could be considered outliers within larger sets.
