Stan Young

US presidential elections are of interest and sometimes controversial. There is a need to provide context over time, both for total vote and party vote. Our idea is to standardize the vote by the estimated population, the Voter Participation Rate. The benefit of our method is that election cycles are more comparable over time.

From just about any point of view, the 2020 US Presidential Election was unusual: a high number of votes recorded, increased mail-in/absentee voting, voting rules changed, etc. Now that the 2024 vote totals are in, we can examine the 2020 voting in relation to the 2016 and 2024 vote counts. The voter participation rate, VPR, the percentage of the population voting, was calculated for the years 2016, 2020, and 2024 by party, Democrat, D, and Republican, R, for each state. Next, the 2020 VPR by party and state was compared to the average VPR for the same party and state by subtracting the averages for the years 2016 and 2024 from the 2020 figure. One would expect that the relative increase, jump or decrease, averaged over the states would be zero, i.e., on average, the VPR for Ds and Rs relative to the 2016 and 2014 elections would be the same. In fact, the average jump in voting (in voter participation rate) was larger for Democrats than for Republicans, 3.09 vs 1.29, a 1.80 advantage for Democrats. A statistical test indicated the difference was beyond chance as an explanation.

Even if a research claim in a scientific study is accepted as true, it may, in fact, be false. There are many contradictory claims in the published science literature, and some end up being taken as true and canonized. These situations can be considered research puzzles. Which of the two opposing claims is correct? Maybe neither is correct. How these puzzles come about and what might be done to see how puzzle parts fit together is an important current question. In this research protocol, we give parts/steps whereby false claims can be identified and hopefully resolved. Claim A or B could be true, or both could be true but under different circumstances.

Large Language Models, LLMs, and Gen AI Technology are being used to improve the efficiency of research. LLMs are trained on vast amounts of data, including research papers, so they can provide a window into a research topic. Here a User queries ChatGPT, a LLM, on the nature of a Data Dredge.

The Southern California Children’s Health Study (CHS) was started in 1992 and continues to this time. Currently, it contains three cohorts recruited across 16 southern Californian communities and included in a follow study in adulthood:

• fourth graders (aged ~9-10 years) in 1992-1993 (cohort “C”)
• fourth graders (aged ~9-10 years) in 1995-1996 (cohort “D”)
• kindergarteners and first graders (aged ~5-7 years) in 2002-2003 (cohort “E”)

Beginning in their respective baseline year and continuing until high school graduation, annual or biannual questionnaires collected a range of information, including demographic characteristics, housing characteristics, prevalence and incidence of bronchitic symptoms, report of doctor-diagnosed asthma, family history of asthma, history of smoking in the household, and residential history.

What is the size of the analysis search space of randomly selected studies cited in Expert testimony to courts about air quality−adverse health effects?

Men outnumber women in many high-status, high-tech fields, e.g., Science, Technology, Engineering, and Mathematics (STEM) and medical professorships. It is often assumed that men and women are equal in all relevant aspects of ability and interest, so blame has been placed on implicit (subconscious) bias for these observed differences. Implicit bias is measured using the gender Implicit Association Test, gIAT. Since measured gIAT is reportedly high, it has been assumed that implicit bias is an important factor in gender difference. We plan to evaluate this current paradigm.

1. How reliable is the gIAT?
2. Does the gIAT correlate well with explicit measures of gender difference and real-world gender actions?
3. How much gender difference variance is accounted for by the gIAT?

The Implicit Association Test, IAT, came into being in the late 1990s, Greenwald et al. 1998. It is a visual and speed reaction test where the subject associates words and pictures. It purports to measure unconscious bias of some sort, e.g. race, gender, obesity, social status, etc. IAT has become one of the most popular subjects of psychology research. A Google Scholar search on 19June2023 found over 40,000 papers with the words “Implicit Association Test” in the paper. Several researchers have called into question the entire IAT scientific enterprise. There are two points of view. On one side, in favor of the IAT, are the originators of the test, Drs. Anthony Greenwald and Mahzarin Banaji, Greenwald et al. 2009. On the other side, calling the test into question, are Drs Philip E. Tetlock, Gregory Mitchell, Oswald et al. 2013, and Ulrich Schimmack 2019.

Reanalysis of several meta-analysis papers dealing with the effects of lockdowns

Many Systematic Review and Meta-analysis, SRMA, studies based on observational studies are appearing in the literature. There is a need to be able to evaluate the reliability of a SRMA. Starting with the risk ratios and confidence limits, we compute a p-value for each study and examine the distribution of the p-values using a p-value plot. In this paper we examine four SRMAs dealing with the effects of COVID lockdowns on humans.

Many claims made by researchers often fail to replicate when tested rigorously. In short, these claims can be wrong. There is a need for students (and citizens) to understand how incorrect claims can come about in research by using questionable research practices. Ask a lot of questions of a data set and make a claim if any p-value is less than 0.05 is p-hacking. HARKing is making up a claim/narrative after looking at the data set. Statistical experts know about p-hacking and HARKing, but they appear to be largely silent. Some researchers know too but they ignore the problem. We present a hands-on demo about rolling ten-sided dice multiple times to show how incorrect claims come about. Several individuals executed simulations of p-values with ten-sided dice and show how easily a small p-value can come about by chance for a modest number of questions (rolls of the dice). Notably, small p-values found by one individual were not replicated by other individuals. These simple simulations allow students (and citizens) to better judge the reliability of a science claim when multiple questions are asked of a data set.