Part of our job as scientists is to provide voluntary peer reviews of articles that have been submitted to journals to provide feedback on the scientific quality of the article. This week you will collaborate as a team of reviewers of the study on weight loss and chocolate.

Based on what you have learned about data analysis so far in the course can you suggest any ways to strengthen this paper? Is there any important information missing? What is the N? Are there other demographics to note? Was the statistical analysis done appropriately?

You do not need to wait until your team meeting to get started and you should return to the Padlet a couple of times through out the week to see what your teammates have said and to provide new comments. 

I haven't provided question prompts this time. You should use text posts rather than videos this time unless you really want to use a video - go ahead. Photos illustrating your points are great too! 

This assignment is worth 15 points. I'd like you each to make 3 posts at two different times this week (feel free to revisit more often) for 5pts each. 

It should clarify the Study Design, whether it is an experimental study or observational study, each type has different strengths and weaknesses. The intervention methods such as the amount and frequency of high cocoa content chocolate consumption should be included to make a better compare with control group.

It should also include a comprehensive literature review that discusses previous studies on the relationship between chocolate consumption and weight loss. This will provide context and support for the study's hypothesis.

The detailed statistical analysis should also be added, apart from t-tests, we can also use ANOVA or regression based on the data types and distributions. Discussions about checks for assumptions such as normality, homogeneity of variances, and independence of observations are important.

The essay should also include discuss any potential biases (e.g., selection bias, measurement bias) and confounders that could affect the results. The scope of applicability for the results should be pointed out, including limitations due to the sample characteristics.

To help better realize data Visualization, we can use more tables and figures to present complex data more accessibly, aiding in the interpretation of results, like bar charts, boxplot and histograms.

Another thing we could do is use randomization when we're assigning people to different groups in our study. This helps ensure that our results aren't skewed by factors we didn't account for. It's all about making sure our findings truly reflect what's going on and aren't just coincidental.