Tag Archives: science

Blind testing of bicycle fitting

Claims that getting a professional bike fit significantly improves riding comfort and speed and reduces overuse injuries seem suspicious – how can a centimetre here or there make such a large difference? A very wrong fit (e.g. an adult using a children’s bike) of course creates big problems, but most people can adjust their bike to a reasonable fit based on a few online suggestions.

To determine the actual benefit of a bike fit requires a randomised trial: have professionals determine the bike fit for a large enough sample of riders, measure and record the objective parameters of the fit (centimetres of seatpost out of the seat tube, handlebar height from the ground, pedal crank length, etc). Then randomly change the fit by a few centimetres or leave it unchanged, without the cyclist knowing, and let the rider test the bike. Record the speed, ask the rider to rate the comfort, fatigue, etc. Repeat for several random changes in fit. Statistically test whether the average speed, comfort rating and other outcome variables across the sample of riders are better with the actual fit or with small random changes. To eliminate the placebo effect, blind testing is important – the cyclists should not know whether and how the fit has been changed.

Another approach is to have each rider test a large sample of different bike fits, find the best one empirically, record its objective parameters and then have a sample of professional fitters (who should not know what empirical fit was found) choose the best fit. Test statistically whether the professionals choose the same fit as the cyclist.

A simpler trial that does not quite answer the question of interest checks the consistency of different bike fitters. The same person with the same bike in the same initial configuration goes to various fitters and asks them to choose a fit. After each fitting, the objective sizing of the bike is recorded and then the bike is returned to the initial configuration before the next fit. The test is whether all fitters choose approximately the same parameters. Inconsistency implies that most fitters cannot figure out the objectively best fit, but consistency does not imply that the consensus of the fitters is the optimal sizing. They could all be wrong the same way – consistency is insufficient to answer the question of interest.

Committing to an experimental design without revealing it

Pre-registering an experiment in a public registry of clinical trials keeps the experimenters honest (avoids ex post modifications of hypotheses to fit the data and “cherry-picking” the data by removing “outliers”), but unfortunately reveals information to competing research groups. This is an especially relevant concern in commercial R&D.

The same verifiability of honesty could be achieved without revealing scientific details by initially publicly distributing an encrypted description of the experiment, and after finishing the research, publishing the encryption key. Ex post, everyone can check that the specified experimental design was followed and all variables reported (no p-hacking). Ex ante, competitors do not know the trial details, so cannot copy it or infer the research direction.

Blind testing of clothes

Inspired by blind taste testing, manufacturers’ claims about clothes could be tested by subjects blinded to what they are wearing. The test would work as follows. People put clothes on by feel with their eyes closed or in a pitch dark room and wear other clothes on top of the item to be tested. Thus the subjects cannot see what they are wearing. They then rate the comfort, warmth, weight, softness and other physical aspects of the garment. This would help consumers select the most practical clothing and keep advertising somewhat more honest than heretofore. For example, many socks are advertised as warm, but based on my experience, many of them do not live up to the hype. I would be willing to pay a small amount for data about past wearers’ experience. Online reviews are notoriously emotional and biased.

Some aspects of clothes can also be measured objectively – warmth is one of these, measured by heat flow through the garment per unit of area. Such data is unfortunately rarely reported. The physical measurements to conduct on clothes require some thought, to make these correspond to the wearing experience. For example, if clothes are thicker in some parts, then their insulation should be measured in multiple places. Some parts of the garment may usually be worn with more layers under or over it than others, which may affect the required warmth of different areas of the clothing item differently. Sweat may change the insulation properties dramatically, e.g. for cotton. Windproofness matters for whether windchill can be felt. All this needs taking into account when converting physical measurements to how the clothes feel.

Keeping an open mind and intellectual honesty

„Keep an open mind” is often used as an argument against science, or to justify ignoring evidence more broadly. Let’s distinguish two cases of keeping an open mind: before vs after the evidence comes in. It is good to keep an open mind before data is obtained – no hypothesis is ruled out. In reality, all possibilities have positive probability, no matter how great the amount and quality of information, so one should not dogmatically rule out anything even given the best evidence. However, for practical purposes a small enough probability is the same as zero. Decisions have to be made constantly (choosing not to decide is also a decision), so after enough scientific information is available, it is optimal to make up one’s mind, instead of keeping it open.
Intellectually honest people who want to keep an open mind after obtaining evidence would commit to it from the start: publicly say that no matter what the data shows in the future, they will ignore it and keep an open mind. Similarly, the intellectually honest who plan to make up their mind would also commit, in this case to a policy along the lines of „if the evidence says A, then do this, but if the evidence says B, then that”. The latter policy resembles (parts of) the scientific method.
The anti-science or just intellectually dishonest way of “keeping an open mind” is to do this if and only if the evidence disagrees with one’s prior views. In other words, favourable data is accepted, but unfavourable ignored, justifying the ignoring with the open mind excuse. In debates, the side that runs out of arguments and is about to lose is usually the one who recommends an open mind, and only at that late stage of the debate and conditional on own weak position. Similarly, “agreeing to disagree” is mostly recommended intellectually dishonestly by the losing side of an argument, to attempt to leave the outcome uncertain. This is an almost logically contradictory use of “agreeing to disagree”, because it is mathematically proven that rational agents putting positive probability on the same events cannot agree to disagree – if their posterior beliefs are common knowledge, then these must coincide.

Seasonings may reduce the variety of diet

Animals may evolve a preference for a varied diet in order to get the many nutrients they need. A test of this on mice would be whether their preference for different grains is negatively autocorrelated, i.e. they are less likely to choose a food if they have eaten more of it recently.

Variety is perceived mainly through taste, so the mechanism via which the preference for a varied diet probably operates is that consuming a substance repeatedly makes its taste less pleasant for the next meal. Spices and other flavourings can make the same food seem different, so may interfere with variety-seeking, essentially by deceiving the taste. A test of this on mice would flavour the same grain differently and check whether this attenuates the negative autocorrelation of consumption, both when other grains are available and when not.

If seasonings reduce variety-seeking, then access to spices may lead people to consume a more monotonous diet, which may be less healthy. A test of this hypothesis is whether increased access to flavourings leads to more obesity, especially among those constrained to eat similar foods over time. The constraint may be poverty (only a few cheap foods are affordable) or physical access (living in a remote, unpopulated area).

A preference for variety explains why monotonous diets, such as Atkins, may help lose weight: eating similar food repeatedly gets boring, so the dieter eats less.

Ways in which an eater can get negative calories from food

There are at least four ways in which an eater may have less energy and nutrients after consuming a food: mechanical, chemical, physical and biological. The mechanical way is that chewing and other parts of digestion take energy, so if a food requires serious mastication and contains few calories, then more energy may be spent than absorbed. This has been claimed for raw celery.
Chemically, one food may react with another in a way that makes one or both of them less digestible. The less effective absorption reduces the nutrients obtained compared to not eating the second reactant. The chemical pathway to inefficient digestion may have multiple steps. For example, ascorbic acid leaches calcium from the body, and calcium is required for the absorption of vitamin D, so eating more citrus fruits may indirectly reduce one’s vitamin D levels.
When calculating the calorie content of food, indigestible fibre is subtracted from carbohydrates before adding up the energy obtained from carbohydrates, fats and proteins. However, if fibre reduces the absorption of calories (in addition to its known reduction of the absorption iron, zinc, magnesium, calcium and phosphorus), then the food’s bioavailable calorie content is less than that obtained by simply subtracting the fibre. To derive the correct calorie content, the fibre should then have negative weight in the calculation, not zero. This difference may explain why in Western countries, a high-fibre diet predicts better health in multiple dimensions in large prospective studies (Nurses’ Health Study, Framingham Heart Study), controlling for calorie intake, lifestyle and many other factors. If the calorie absorption is overestimated for people eating lots of fibre (because the calorie intake is larger than the absorption), then their predicted health based on the too high calorie estimate is worse than their actual health. This is because most people in Western countries overeat, so eating less improves health outcomes. If the predicted health is underestimated, then the high-fibre group looks unusually healthy, which is attributed to the beneficial effects of fibre, but may actually be due to absorbing fewer calories.
A food may chemically break down tissues, e.g. bromelain and papain, from fresh pineapple and papaya respectively, denature meat proteins, so cause mouth sores. Rebuilding the damaged tissue requires the energy and nutrients, the quantity of which may exceed that absorbed from the food.
Chemically causing diarrhea reduces the time that foods (including the laxative agent) spend in the gut, thus reduces nutrient absorption.
Stimulants like caffeine speed up metabolism and cause greater energy expenditure, but may give zero calories themselves, resulting in a net negative caloric balance.
Just like chemical damage, physical injury to the body necessitates spending calories and nutrients for tissue repair. For example, scratchy food (phytoliths, bran) may cause many microscopic wounds to the digestive tract.
Cold food requires the body to spend energy on heating, so if the calorie content is small, then the net energy obtained is is negative. Examples are ice cubes and cold water.
A food substance may physically partially block the absorption of another, for example a gelling agent (methylcellulose, psyllium husks) may turn a juice into a gel in the gut and thereby reduce its absorption. Based on my personal experience, psyllium husks gel liquid feces, thus effectively reducing diarrhea. Mixing psyllium husks with carrot juice and with asparagus powder dissolved in water before consuming them during the same meal results in the excretion of separated faint orange and green gels somewhat distinct from the rest of the feces (photos available upon request, not posted to keep the blog family-friendly). This is suggestive evidence that the gelling agent both kept the juices from mixing in the gut and reduced the absorption of the colourful compounds by keeping the juice in the centre of the gel away from the intestinal wall.
Biologically, a food may reduce the nutrients available to the organism by causing infection, the immune response to which requires energy and depletes the body’s reserves of various substances. Infection may lead to diarrhea, although the mechanism is chemical, namely the toxins excreted by the microbes. Infection with helminths (intestinal worms) that suck blood through the wall of the gut requires the replenishment of blood cells, which uses up calories, protein and iron.
If the food takes a long time to chew or is bulky, then chemical and electrical signals of satiation are sent from the gastrointestinal tract to the the appetite centre of the brain. These signals reduce the desire to eat, thus decrease calorie intake.

Bad popular science books

There is a class of books that is marketed as popular science, but have the profit from sales as their only goal, disregarding truth. Easily visible signs of these are titles that include clickbait keywords (sex, seduction, death, fear, apocalypse, diet), controversial or emotional topics (evolution, health, psychology theories, war, terrorism), radical statements about these topics (statements opposite to mainstream thinking, common sense or previous research), and big claims about the authors’ qualifications that are actually hollow (PhD from an obscure institution or not in the field of the book). The authors typically include a journalist (or writer, or some other professional marketer of narratives) and a person that seems to be qualified in the field of the book. Of course these signs are an imperfect signal, but their usefulness is that they are visible from the covers.
Inside such a book, the authors cherry-pick pieces of science and non-science that support the claim that the book makes, and ignore contradicting evidence, even if that evidence is present in the same research articles that the book cites as supporting it. Most pages promise that soon the book will prove the claims that are made on that page, but somehow the book never gets to the proof. It just presents more unfounded claims.
A book of this class does not define its central concepts or claims precisely, so it can flexibly interpret previous research as supporting its claims. The book does not make precise what would constitute evidence refuting its claim, but sets up “straw-man” counterarguments to its claim and refutes them (mischaracterising the actual counterarguments to make them look ridiculous).
Examples of these books that I have read to some extent before becoming exasperated by their demagoguery: Sex at dawn, Games people play.

Heating my apartment with a gas stove

There is no built-in heating system in my Australian-standard un-insulated apartment, and the plug-in electric radiators do not have enough power to raise the temperature by a degree. In the past two winters, I used the gas stove as a heater. It is generally unwise to heat an enclosed space without purpose-built ventilation (such as a chimney) by burning something, because of the risk of CO poisoning. Even before CO becomes a problem, suffocation may occur because the CO2 concentration rises and oxygen concentration falls. Therefore, before deciding to heat with a gas stove, I looked up the research, made thorough calculations and checked them several times. I also bought a CO detector, tested it and placed it next to the gas stove. The ceiling has a smoke alarm permanently attached, but this only detects soot in the air, not gases like CO.
For the calculations, I looked up how much heat is produced by burning a cubic metre or kilogram of CH4 (natural gas), how much the temperature of the air in the apartment should rise as a result, how much CO2 the burning produces, and what the safe limits of long-term CO2 exposure are.
The energy content of CH4 is 37.2 MJ/m3, equivalently 50-55.5 MJ/kg. A pilot light of a water heater is estimated to produce 5.3 kWh/day = 20 MJ/day of heat, but a gas stove’s biggest burner turned fully on is estimated to produce 5-15 MJ/h, depending on the stove and the data source.
The chemical reaction of burning natural gas when oxygen is not a limiting factor is CH4 +2*O2 =CO2 +2*H2O. The molar masses of these gases are CH4=16 g/mol, O2=32 g/mol, CO2=44 g/mol, H2O=18 g/mol, air 29 g/mol. One stove burner on full for 1 hour uses about 0.182 kg =0.255 m3 of CH4 and 0.364 kg of O2, which depletes 1.82 kg = 1.52 m3 of air. The burning produces 2.75*0.182 = 0.5 kg = 0.41 m3 of CO2. The CO2 is denser than air, which is why it may remain in the apartment and displace air when the cracks around the windows are relatively high up. On the other hand, the CO2 also mixes with the air, so may escape at the same rate. Or alternatively, the CO2 is hot, so may rise and escape faster than air. For safety calculations, I want to use a conservative estimate, so assume that the CO2 remains in the apartment.
The volume of the apartment is 6x5x2.5 m =75 m^3. The density of air at room temperature is 1.2 kg/m^3, thus the mass of air in the apartment is 90 kg. The specific heat of air is 1005 kJ/(kg*K) at 20C. The walls and ceiling leak heat, thus more energy is actually needed to heat the apartment by a given amount than the calculation using only air shows. It takes 900 kJ of heat to raise the temperature of the air, not the walls, by 10C (from 12C to 22C). This requires 9/555 kg = 9/(16*555) kmol of CH4 with estimated energy density 55500 kJ/kg. Burning that CH4 also takes 9/(8*555) kmol of O2 and produces 9*11/(4*555) kmol = 9/200 kg of CO2.
The normal concentration of CO2 in outside air is 350-450 ppm. Estimate the baseline concentration in inside air to be 1/2000 ppm because of breathing and poor ventilation. Adding 1/2000 ppm from heating, the CO2 concentration reaches 1/1000 ppm. This is below the legal limit for long-term exposure.
CO is produced in low-oxygen burning. As long as the CO2 concentration in the air is low and the oxygen concentration high, the risk of CO poisoning is small.
For the actual heating, I first tested running the smallest burner all day while I was at home, and paid attention to whether I felt sleepy and whether the air in the apartment smelled more stale than outside or in the corridor. There seemed to be no problems. For nighttime heating, I started with the smallest burner in the lowest setting, similarly paying attention to whether the air in the morning smelled staler than usual and whether I felt any different. Because there were no problems, I gradually increased the heating from week to week. The maximum I reached was to turn on the largest burner to less than half power, and one or two smaller burners fully. Together, these burners produced much less heat than the largest burner on full, as could be easily checked by feel when standing next to the stove. At night, the stove prevented the temperature in the apartment from dropping by the usual 2C, but did not increase it. The CO2 produced was probably far less than the bound I calculated above by assuming a 10C increase in temperature. Empirically, I’m still alive after two winters of letting the gas stove run overnight.

How superstition grows out of science

Priests in Ancient Egypt could predict eclipses and the floods of the Nile by observing the stars and the Moon and recording their previous positions when the events of interest happened. The rest was calculation, nothing magical. Ordinary people saw the priests looking at the stars and predicting events in the future, and thought that the stars magically told priests things and that the prediction ability extended to all future events (births, deaths, outcomes of battles). The priests encouraged this belief, because it gave them more power. This is one way astrology could have developed – by distorting and exaggerating the science of astronomy. Another way is via navigators telling the latitude of a ship using the stars or the sun. People would have thought that if heavenly bodies could tell a navigator his location on the open sea, then why not other secrets?
Engineers in Ancient Rome calculated the strength of bridges and aqueducts, and estimated the amount of material needed for these works. Ordinary people saw the engineers playing with numbers and predicting the amount of stones needed for a house or a fort. Numbers “magically” told engineers about the future, and ordinary people thought this prediction ability extended to all future events. Thus the belief in numerology could have been born.
When certain plants were discovered to have medicinal properties against certain diseases, then swindlers imitated doctors by claiming that other natural substances were powerful cures against whatever diseases. The charlatans and snake oil salesmen distorted and exaggerated medicine.
Doctors diagnosed diseases by physical examination before laboratory tests were invented. Thus a doctor could look at parts of a person’s body, tell what diseases the person had, and predict the symptoms that the person would experience in the future. Exaggerating this, palm readers claimed to predict a person’s future life course by looking at the skin of their palm.
In the 20th century, some medicines were discovered to be equally effective at somewhat lower doses than previously thought. Then homeopathy exaggerated this by claiming that medicines are effective when diluted so much that on average not a single molecule of the drug remains in the water given to the patient.
In all these cases, superstition only adds bias and noise to scientific results. Science does not know everything, but it is a sufficient statistic (https://en.wikipedia.org/wiki/Sufficient_statistic) for superstitious beliefs, in the sense that any true information contained in superstition is also contained in science. Nothing additional can be learned from superstition once the scientific results are known.

Scientific thinking coordination game

If most people in a society use the scientific method for decision-making, then telling stories will not persuade them – they will demand evidence. In that case, bullshit artists and storytellers will not have much influence. It is then profitable to learn to provide evidence, which is positively correlated with learning to understand and use evidence. If young people respond to incentives and want to become influential in society (get a high income and social status), then young people will learn and use the scientific method, which reinforces the demand for evidence and reduces the demand for narratives.
If most people are not scientifically minded, but believe stories, then it is profitable to learn to tell stories. The skilled storytellers will be able to manipulate people, thus will gain wealth and power. Young people who want to climb the social and income ladder will then gravitate towards narrative fields of study. They will not learn to understand and use evidence, which reinforces the low demand for evidence.
Both the scientific and the narrative society are self-reinforcing, thus there is a coordination game of people choosing to become evidence-users or storytellers. Note that using the scientific method does not mean being a scientist. Most researchers who I have met do not use science in their everyday decisions, but believe the stories they read in the media or hear from their friends. I have met Yale PhD-s in STEM fields who held beliefs that most people in the world would agree to be false.
One signal of not thinking scientifically is asking people what the weather is like in some place one has not visited (I don’t mean asking in order to make small talk, but asking to gain information). Weather statistics for most places in the world are available online and are much more accurate than acquaintances’ opinions of the weather. This is because weather statistics are based on a much longer time series and on physically measured temperature, rainfall, wind, etc, not on a person’s guess of these variables.