GS5002: Academic Professional Skills and Techniques, "Journal Club on Big Data and a Bewildered Lay Analyst", 2021 (AY21/22 Sem 1)

Session #4, 7 September 2021: Kanazawa, "Beautiful parents have more daughters: A further implication of the generalized Trivers-Willard hypothesis (gTWH)"

The paper by Kanazawa discusses a very interesting topic regarding the parents’ ability to vary the sex ratio of their offspring in respond to their conditions. From there, the author predicts that physically attractive parents have more daughters. And over evolutionary history, women should gradually become more attractive than men. The author claimed that their analysis confirmed both hypotheses, but is that true?

Organization of the session, lead by Nguyen Thuan Tinh

Part I, Background information:

This part deals with background knowledge of cancer biology and statistics that is needed to understand the paper. Some keywords are highlighted below to look up background literature, Wikipedia, etc.

• Natural selection
• Trivers and Willard’s hypotheses
• Generalized Trivers–Willard hypothesis and evidence

Part II, The paper by Kanazawa.

This part presents the paper itself. We want to know the key technical details and the key messages.

• Details of how Kanazawa carry out the study
• What the main findings of Kanazawa are
• Any evidence arising since providing support/ contradiction?

Part III, Possible points for discussion

This part discusses the Vul et al. paper, hopefully in depth. We want to know whether there is any methodological issue, any doubt on the conclusions/key messages, any suggestion for improving the paper. Some pointers for discussion include:

• Any methodological issue? E.g. have they overlooked anything?
• Any issue on the key messages? E.g. do their results support their conclusions?
• Future work? E.g. If there have been issues, what should be done to properly investigate the hypothesis?

Instructions

The journal club has 4 sessions. We will discuss only 1 paper in each session. I will pick the paper for the 1st session, to set the scene. Hopefully, you will suggest the papers for the subsequent 3 sessions (we will choose by a simple vote from among the suitable papers you suggest.) Any paper can be suggested, so long as it (i) concerns data analysis (esp. big data) and (ii) contains “controversial” analysis or methodological issues that you think are worth for your classmates to appreciate.

For each paper, the presentation is divided into 3 parts: (i) background of the topic/paper – to help students who lacks domain knowledge, (ii) the paper itself - focusing on technical details, and (iii) discussion on the paper. And you will be divided into 3 teams, each team presents one part. The team will rotate through the 3 roles over the 4 sessions. This also mean everyone has to read every paper (plus some related papers/webpages which are helpful for understanding the paper being discussed.)

The grading will be based on presentation (50%), asking and answering questions during the discussion (50%).

Wong Limsoon
3 September 2021