Barak Obama, Bill Gates, Oprah Winfrey, Babe Ruth, and Marie Curie are notable individuals who are left-handed, showcasing that left-handedness is not uncommon among prominent figures.

According to a 1991 study, left-handed people were reported to die on average nine years earlier than right-handed people, a claim that warrants further investigation.

The aim of this project is to explore the potential link between left-handedness and average age at death using age distribution data. We will utilize Bayesian statistics and pandas to analyze the probability of reaching a certain age at death based on reported hand preferences (left-handed or right-handed).

In 1986, researchers Avery Gilbert and Charles Wysocki conducted a National Geographic survey with over a million responses, collecting data on age, sex, and hand preference for throwing and writing. The study revealed that rates of left-handedness were approximately 13% among people younger than 40 but decreased with age, reaching about 5% by the age of 80. Gilbert and Wysocki inferred that this age-dependence was primarily due to changing social acceptability of left-handedness, suggesting that the rates were more influenced by the year of birth rather than age specialization. If the same study were conducted today, we would expect a similar distribution, but with a shift related to changing rates of left-handedness over time. Ultimately, this project aims to determine the effect of this changing rate on the apparent mean age at death.

To conduct this analysis, two datasets will be used: one containing death distribution data and another containing rates of left-handedness. By examining these datasets and applying Bayesian statistics, we can gain insights into whether left-handedness is indeed associated with an earlier average age at death or if the observed difference is a result of changing rates of left-handedness across different generations.