Reply 6-1 KiTe (100 words and 1 refere4nce)
According to Qualtrics (2022), "ANOVA" refers to "Analysis of Variance." This statistical test was developed in 1918 by Ronald Fisher and has been utilized ever since (Qualtrics. 2022). A researcher can determine whether there are any statistical differences between three or more independent groups using a straightforward ANOVA. A factorial design is a study design in which two or more independent variables are crossed to create the study conditions. Therefore, if there were two independent variables, such as A and B, with two levels, the study conditions would be expanded to include every possible combination of those levels: B2, A1, A2, and A3. A cross-design like this one necessitates the study to investigate independent variables and their interactive effect on any dependent variables, which adds additional work. Errors are more likely to occur because there are more variables to test. The ANOVA test is helpful when there are statistically significant differences between data samples, just like the t-test. One excellent example is: Does a person's income, gender, or age influence how much they spend in your store each month (Qualtrics, 2022)? Factorial ANOVA would be used to test this because there are three independent and one dependent variable. To evaluate various income levels and relevant sexes, it would be necessary to use multiple age groups, such as 21 to 30, 31 to 40, and 51 to 60.
Reply 6-2 ChRo (100 words and 1 reference)
An ANOVA test can have an impact on reducing the likelihood of having a type I error. This is because they are made to test multiple sets of data and compare them to one another at the same time. This is to prevent the probability of the significance level from causing a false negative to occur and the null hypothesis being rejected when it should have been accepted. When an ANOVA test is run it already accounts for the significance level so there is less to work about in comparison to a t test. When doing a t test, every time is it run the significance level is how likely a type I error could occur. If there are multiple group this then is multiplied, which can make it where a type I error is more probable to happen compared to if and ANOVA test is used.
Reply 6-1 ChRo (100 words and 1 reference)
A one-way ANOVA test can be used to compare population mean depending on the factors involved and can be very informative if used properly. When using a two-way ANOVA, the main thing that is being compared is the interactions between the different factors and their levels. The reason that factorial designs with two or more independent variables can induce errors is since other variables can have an impact on results. With that not being considered, when the results are being presented, they would not consider the other factors that played a role. An example would be in an experiment that looked at the effects of age on activity level. You would take different age groups and see the differences in the activity level based on the age ranges of the people.
