Assignent Solutionnurs

Below are peer discussions that I have to respond to like opinions and etc, i have attached my sample test on what mines was:
jennifer
6:38pmFeb 6 at 6:38pm
Hi class,
1. How many participants?
I used the maximum amount of data I can use on JASP which was twenty different sets. This is because the more data you have is usually more helpful and reveals a lot more and prevents unusual data due to outliers. Overall, I had a high number of participants which was 292 total. Then I proceeded to divide them into female and male to see if there was a difference due to these two factors.
2. How many male and how many female?
As mentioned above I had a total of 292 participants. In this total, I had 216 females and 76 males. This number definitely bothers me because since the numbers aren’t balanced that may mean the slightest outlier in the men’s data may sway the overall result. Another factor I dealt with was originally I computed the ANOVA with transgender and non confirming. These results were also biased since their numbers were so small. Although, I went back and fixed it the results were definitely swayed due to a small number.
3. What were the results of your ANOVA?
The results of my ANOVA test showed that if our null hypothesis is gender plays a role in the results it is supported by my P-value. Surprisingly my P-value for gender was pretty high .604 for the within-subjects. Meaning if a p-value is below 0.05 we can typically reject the null hypothesis. Interestingly though the between-subject variable is quite low in my data reporting a p-value of .020. This is data when considering individually. Another interesting fact I found was that my two plots failed to intersect. Overall, this is the data I collected is interesting and would love some help translating!
SECOND PEER:
Katelynn Moore
ThursdayFeb 3 at 6:38pm
Within the sample there are a total of 276 participants. The sample population consists of 200 females and 76 males.
Results:
(1) There is a significance of main effect for within-subjects, F(2, 825) = 17.86, p < .00. (2) However there is no significant interaction, F(2, 825) = 0.89, p = 0.41. (3) The results also do not support a significant main effect for between-subjects, F(1, 274) = 0.28, p = 0.60. For this study, it seems like we would have to fail to reject the null hypothesis since there is no significant difference between the means of the populations. Although the lack of males within this sample might have an influence on the results. Even though the data shows that there is no significant relationship between the male population, this might be a Type I Error. This often happens when the sample population is relatively small because it is more difficult to prove a correlation with a small population.