What would raise my eyebrows is not using real world examples to help children understand mathematics. There are very few people who can handle completely abstr

What would raise my eyebrows is not using real world examples to help children understand mathematics. There are very few people who can handle completely abstract mathematics at any age, and it has been necessary to use examples such as counting rocks or sticks to teach basic numeracy since language was invented. This need to ground the instruction in practical examples applies especially to statistics in schools where the most relatable population for discussing populations is the human population. The history of erasing inconvenient populations by various deliberate or accidental means such as focussing on outcomes rather than causes is something that absolutely does need to be taught as part of a statistics course. When studying the physical properties of gasses we don’t ignore the spurious peaks in excitation data, we look for explanations as to why those two peaks in the hydrogen excitation experiment occurred. Binning by wider excitation ranges would have helped erase those anomalies, but the scientists chose to ask the question rather than hide the uncomfortable facts (“eliminate the outliers”). We necessarily should be asking political questions in statistics because most statistics are innately tied to human behaviour, not pure mathematics or science. Are there more people working in maths and physics or law enforcement and legislation? The best way to educate future statisticians about invisible populations is to teach them about the mistakes of the past. These mistakes include for example focussing on which current populations commit the most violent domestic crimes per capita without thinking to ask why people commit crime, and then comparing per capita crime rates to the various causative factors of crime (which might be for example wealth, access to employment, police hours per capita). Everything is politics, any time there are one or more people involved. There is no escaping this even in mathematics. To pretend otherwise is to reinforce the erasure of oppressed populations such as women and non-majority races/cultures (and note that using the word “cultures” instead of “races” doesn’t stop you being racist). Essay topic for today: is there race or gender discrimination in physics and maths employment in the USA? Why are there so few women in STEM and where do populations start to become more distinct? Is the bimodal distribution of various characteristics by sex sufficient to explain the male dominance of mathematics and physics and the rapid decline of female populations from high school through to the end of a bachelors degree? Are there factors apart from IQ that can explain the lack of women in STEM?