Assignment task:
175 words each
Discussion post 1:
HI Class, Math has always been something I try to avoid but here we go again. So I gathered I need to take the data of chocolate chips from Chips Amor cookies verses local grocery store cookies to compare and see which business has better cookies than the other. In order to do that I need to use the data to create a histogram and explain my findings on whether what parameter are being compared, writing a null hypothesis and alternative hypothesis, showing what populations the samples came from, whether my hypothesis is one-tailed or two-tailed and if the samples are random or independent of each other. That is a ton of information so I hope I touch on it all. The parameters that are being compared are the Chips Amor and the local grocery store. The null hypothesis measured the significant difference between the two companies with null meaning there is no significant difference as it relates to the companies, while alternative hypothesis is the contradiction of null hypothesis. The population that the samples came from was pop A-Chips Amor cookies and pop B- local grocery store cookies and this was done via the one-tailed test. I believe the samples are both random and independent of each other. For some reason i'm not able to attach my histogram here, sorry about that. Need Assignment Help?
Discussion 2:
Chocolate Chip Comparison: Hypothesis Testing for Chips Amore
Since I am Chips Amore product manager, I have been tasked with determining if our cookies really contain more chocolate chips than those found in a local supermarket store brand. To put this claim to the test, I conducted a taste test of 30 subjects. Each subject was given a Chips Amore cookie (Cookie A) and a local store-brand cookie (Cookie B) and asked to enumerate the chocolate chips on both.
The test parameter is the mean number of chips in a chocolate cookie. Since each subject tested both brands, samples are dependent (paired). The design is ideal for a paired sample t-test applied to examine the difference between the means of two related samples.
The test hypotheses are:
Null Hypothesis (H0): μ1 = μ2 (There is no variation in the average number of chips.)
Alternative Hypothesis (H1): μ1 > μ2 (Chips Amore has more chips on average.)
This is a one-tailed test because the hypothesis is directional.
Results
Mean (Chips Amore - Cookie A): 23.33 chips
Mean (Store Brand - Cookie B): 21.57 chips
Mean Difference: 1.77 chips
t-statistic: 4.55
p-value: 0.000089
Because the p-value is less than 0.05, I reject the null hypothesis, confirming the assertion that Chips Amore cookies have much more chocolate chips.
As a product manager, these results are compelling evidence for branding and marketing. They validate the relative competitive merit of our product numerically. For functional purposes, these tests can be applied in quality testing, taste preference testing, and product development. I have seen these data analysis being used in pharmaceuticals (relative drug performance), education (test score improvement), and customer satisfaction (before-and-after surveys).
References:
American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). American Psychological Association.