Briefed by Data is a Substack blog and newsletter written by Thomas J. Pfaff. He is a professor of mathematics. Professor Pfaff’s fields of expertise include probability, data science, statistics, and data visualization. Besides teaching, professor Pfaff works as a data scientist working to use advanced analytical techniques to create solutions to various problems. Sine 2001, Thomas Pfaff has taught at Ithaca College. Mathematics can be applied to multiple fields. Pfaff wants to educate the public about statistics and data science. According to Pfaff ” the media are data challenged, and their use of data is either superficial, incomplete, or misleading in some way.” Thomas J. Pfaff wants to encourage data literacy. Pfaff took a data set of male and female grip strength. He condemns the way data can be presented in a misleading way, but might be guilty of it. Looking at this data from the National Health and Nutrition Examination Survey one would assume that women are weaklings. The strength gap does exist between the sexes, but the graphs do not tell you why. Being a mathematician there would be a better data visual representation. Educators are not free of bias. Thomas J. Plaff expresses that the strength gap has a large magnitude. When trying to discover this proxy measurements have a limitation.
Mathematics focuses on numbers, their properties, and computation. The NHANES sample consisted of 8,291 subjects. Thomas J. Pfaff removed subjects with arthritis, carpel tunnel syndrome, or those who had hand surgery. People who had wrist surgery were excluded as well. excluding those with arthritis made sense. Arthritis cannot be cured and can effect grip. People who had hand or wrist surgery should remain, if they recovered. Carpel tunnel syndrome will not cause permanent nerve damage on the condition it is treated properly. The NHANES tested both hands, so it is assumed most people did in the study. The exclusion of data was thus unnecessary. So the number is subtracted to 4, 927 subjects. Women outnumber the men at 2,515. Men are placed at 2, 412 in the sample. With the sample removals, there still is enough to make generalizations. However, removing those who can use both hands distorts the data. The NHANES sample is only a small part of the total US population( 335,893,238) . According to the US Census Bureau there are 166, 000,000 women in the US (2019 estimate) . The women in the NHANES sample represent 0.0015 % of the female population. All people in the NHANES group represent 0.001 % of the US population. Understanding quantity puts this into perspective. The data only shows a small portion of the population and its hand grip capability.
The sample shows that less than 0.1% percent of women have the greater sum median grip strength of men. The median can be defined as data being assorted into lower and higher values. The middle value is the border between the highest and lowest data. The way the graph is depicted can be misleading. Without reading the X or Y axis it would look as if women have the higher grip strength. Another problems is that grip strength units of measurement is not labeled on the X axis. Hand grip can be measured in kilograms (kg) or pounds ( lbs). The Y axis denotes the population sample. When calculated the difference in strength between both sexes is a 31.6 mean. The male grip strength is an 87.9 compared to women’s 56.3. That gives a strength gap as expressed a mean. Thomas J. Pfaff claimed that most people underestimate how large the gap is. A person has to have precise measurement to make that determination. Hand grip is just accurate, not precise.
The strongest women fall between a 60 lbs to 100 lbs grip strength. The strongest male can reach up to 150 lbs.
The data visual representation would be better on scatter plot graph. This way age and hand grip can be all accounted for. The bar graph does not include age. Other bar graphs had to be made to show comparisons. The scatter plot shows all the important information. The bar graph does a better job at showing overlap. The strongest women fall into the 50 lbs to 100 lbs grip range. The strongest women reach the 100 lbs mark and the strongest men 150 lbs. That is a difference of 50 lbs. If the gap were so immense, this should be much bigger. The biggest gap is the weakest woman compared to the strongest man. The strongest woman could be over the 87.9 mean. It assumed that most of the subjects do not do regular exercise. The strongest women could have a high amount of natural strength or have been doing athletic training. The data just shows men have a tendency to have higher muscular strength. This does not precisely quantify the strength gap.
Thomas J. Pfaff does not explain why there is a strength gap. Exercise physiology , sexual dimorphism, and human biological evolution are the obvious answers. There are also social factors that contribute to how large the gap is. Girls and women participate in lower numbers in sports. Girls get less physical activity than boys and physical education has lower standards for them. The reason men are asked to open jars is not a matter of biology only. Boys are encouraged to do rough and tumble play. Title IX was passed being part of the Education Amendments of 1972. This did increase participation, but women’s numbers are lower. Athletes as Thomas J. Pfaff notes are above average. One would have to examine the difference in exercise habits among average people. Data collected from the the National Health and Nutrition Examination Survey revealed that women exercise less than men.
Exercise and physical activity habits change as a person ages. The CDC self report on exercise shows women and girls do less exercise.
This could have an impact on some of that grip strength data. The self reported exercise data shows a difference. A correlation between age and strength does exist. As children grow they get stronger. When adults reach advanced age musculoskeletal mass declines. If exercise activity declines at a large margin in women, then it would directly impact grip strength. Most Americans are not getting enough exercise or ignore the CDC recommendations. The strength gap can be widened by other means. Less caloric intake, body dysmorphia, and eating disorders can disproportionately effect women.
A small problem emerges when comparing sexes across age. Comparing the oldest males to the youngest females creates a contradiction. The grip strength values overlap. The claim was that most underestimate how wide the strength gap is. The strength gap narrows when age is a factor. The overlap between the youngest women and oldest men is significant. The median for males 70 to 80 is 68.55. Women 20 to 29 are 60 median range. That is a difference of 8.55. That value is too small to demonstrate a wide strength gap. The medians reveal three details. Age can drastically reduce muscle mass and strength. Hand grip data should be categorized by age group to account for changes in the body over a life time. Women are not getting enough exercise in youth to build the healthy amount of musculoskeletal mass.
The assumption is that male strength advantage would remain constant . Based on this data men lose a big portion of strength due to age .
The comparison between these two age ranges seem unnecessary. The 31. 6 difference in the amalgamated data shows the magnitude. The 8.55 grip is minor. Using this comparison is not the best way to show the magnitude of the strength gap. The data shows how immensely average male strength declines.
The data is best represented in Gaussian distribution. Thomas J. Pfaff selected bar graphs for statistical representation . A normal distribution model would take into account variation and overlap. Applying Gaussian distribution would not produce the errors seen in different age comparisons. Thomas J. Pfaff was trying to show how large the gap was by comparing young women to old men. The data showed the strength gap diminished. Comparing two different age groups does not make sense, if one is trying to see the magnitude of the strength gap. Making a comparison between 10 year old boys and 30 year old women would just be as flawed. That is why hand grip data is separated by age group.
The strongest woman’s grip strength would overlap with a portion of men. Most would fall in the middle of the graph as the average.
If one wants amalgamated data then normal distribution would be the best mathematical representation. Bar graphs would require separate sets. Thomas J. Pfaff could have mentioned this. Hand grip is a great example of Gaussian distribution. Being a mathematician it is like a missed opportunity to not apply it with the NHANES data.
Using track and field performance is not the best for examining strength difference. Sprinting does involve lower body strength. Olympic weightlifters would have more strength than runners. It would make more sense to use weightlifter statistics. This gives a better idea of the strength gap under athletic conditions. Applying the Sinclair coefficient enables a comparison between weight classes. Women’s collective total 200 kg compared to men’s 300 kg in smallest weight class. Women’s total in the higher weight class is closer to the smallest men. Thomas J. Pfaff was using track and field performance to fill the missing data for lower body strength. A hand grip dynamometer is not going to show the strength of the legs. The largest difference in strength correlates to the upper body among the sexes. The strength difference is not as large in the lower body.
Sprinting does involve lower body strength. It is not an indicator of absolute strength. Using weightlifting records would be more useful than track times.
Squats can give an indication of lower body strength. Again, there is a strength gap. A general strength standard chart shows the difference in relation to bodyweight. The strongest woman falls into a male intermediate range. The gap between the strongest woman and man is 131 kg( 290 lbs) . Thomas J. Pfaff was insisting people underestimate the size of the strength gap. Based on that claim the gap should be 200 kg more. The smallest elite male can squat 145.15 kg compared to the biggest woman’s 138. 3 kg. The difference is only 6.85 kg. The gap is not that large when smaller men are compared to bigger women. Another problem comes with the source that Thomas J. Pfaff used. The Duke University Law school data on running was basically making the argument of excluding transwomen and transgirls from sports. They were using data of cisgender boys to compared to women. The objective was to say transwomen and girls would dominate, but did not provide data of their performance. Then Thomas J. Pfaff mentions the longer the distance fewer males beat females. Discussing endurance is irrelevant, because the focus is on the strength gap.
Thomas J. Pfaff brings up the question of relevance. It only matters in certain occupations and aspects of health. Statistics can be used to justify discrimination or distortions. A person could look at the hand grip data and say women should not be in physically demanding occupations. Hand grip charts were used by those opposed to women in combat as evidence to ban women from certain jobs. The hand grip data should not be used to exclude women from physically demanding occupations. Hand grip should be used to track levels of fitness. Women seeking to build strength should use hand grip to measure fitness progression. Knowing the fitness requirements and making a training program based on that will ensure success. Thomas J. Pfaff may not have an altruistic intention writing about the hand grip data.
Arguments related to strength have been used to exclude women from physically demanding occupations. Statistics have been misused to justify prejudice or discriminatory policies.
The proxy measurement is utilized when direct data collection cannot be done. The National Health and Nutrition Examination Survey did not use isokinetic dynamometers. That type of equipment is more precise. Hand grip dynamometers are used due to low cost and availability. Accuracy only gives a close measurement to an actual value . Hand grip is not a precise value. To show the magnitude of the strength gap would require a precise measurement. Hand grip data has another pivotal application. Hand grip can provide evidence of musculoskeletal health. The total amount of grip force can be a proxy measurement for the condition of bone and muscle in the human body. This matters more, rather than a comparison of physical abilities between the sexes. The reason relates to the difference in sarcopenia and osteoporosis. Women could be at higher risk due to lower amounts of muscle mass. Regular resistance training and keeping track of handgrip can ensure women can maintain their musculoskeletal health. Proper diet and daily exercise can be methods of disease prevention. Hand grip does not provide much information on cognitive health, the status of the circulatory system, or the digestive system. Hand grip would not predict dementia, heart disease, or colon cancer. It can be a tool of myology and preventing diseases of the muscular system . Thomas J. Pfaff accuses people of using data in a misleading way, but appears that he was guilty of this in the hand grip data. Numerous reason exist for the difference in hand grip between the sexes. Physiology and sexual dimorphism are only two of many factors. Exercise activity levels, sports participation, diet, and health condition are also impact strength.