Sunday, 7 February 2016

On Hiking Differences


The biggest difference between hiking in the UK and South/West India is the investment in equipment. 

In Mumbai, and I suppose Pune, Chennai, Bangalore or anywhere in South India, you can afford to be a minimalist when you hike i.e you don't need a lot of stuff. All I used to wear on a day trip from Mumbai was a t-shirt, non-denim trousers, sneakers and a light raincoat. I'd swap the trousers for shorts if it was a shorter hike, involved a beach, flat open ground, or involved wading through water. I'd stick with trousers if it involved forests, shrubbery, thorns and mosquitoes. The fact that most of my hikes took place in the monsoons didn't matter. It was still humid enough to warrant the bare essentials. I did carry a wool pullover on overnight trips just in case it got cold. It was the only time I ever used the pullover normally stashed away in the back of my wardrobe in Mumbai. 

I usually carried a light raincoat. Not among the most durable of apparel, it did its job, which was to keep the bulk of the rain off my body and cotton clothing till the end of my hike. I know guys who hiked shirtless. In a humid monsoon hike, maybe polyester shorts and shoes are all you need. I wore a simple hat to keep the sun out of my eyes and the rain off my glasses. Most people didn't. They found it too hot, irritating or distracting. 

My shoes were initially everyday sneakers. Yeah they weren't the best for rough hiking, but they were great for most of my hikes that involved flat trails. I switched to Woodlands, which helped with longevity. Again, not a priority for casual hikers who stick to flip-flops or sandals. I never considered wearing gaiters. No idea if you can even buy them in India. We just considered water in our shoes a normal unavoidable thing. Gaiters can help keep your shoes dry to an extent, but not when you're shin deep in a flowing river. My socks were normal cotton ones. I never needed insulating ones. There was no cold to protect against. In hindsight I think wearing thick or double pairs of cotton socks would have meant less damage to my toes. 

Contrast this with the UK, where people usually wear professional branded light stretchable hiking trousers that wick rain away. And waterproof overalls over wear over your trousers in heavy rain. And hiking trousers with thick lining on the inside in case it's a winter hike. Or thermal pants will do. Or maybe just lycra running pants. And professional hiking T-shirts that wick sweat away. 

And a professional rain jacket, preferably made of weather-proof Gore-Tex for toughness. One with fleece lining in case you're hiking in cold weather. Or a fleece jumper as a mid-layer if id gets cold. A medium or heavy fleece in cold weather and a micro-fleece in not-so-cold weather. You can just wear the fleece instead of the jacket if it's merely cold but not raining. It's important to carry layers. You'd also need to wear a thermal undershirt if it's a winter hike. Thermal monkey caps or hats that protect your ears against the cold and wind are common. And so are wide brimmed hats that protect against the sun. 

And then there's the shoes. You get weather-resistant hiking shoes (Gore-tex again). And professional thick hiking socks. I usually buy my shoes one or two sizes too large and then wear a couple of thick hiking socks to protect my toes and keep my toenails. The cushioning helps. If you don't hike often, you can always reuse your running or gym equipment. I've seen a lot of people show up for hikes in tights, running/sports jackets and running shoes. I suppose this is OK for day hikes on easy ground. Running/sports clothing tends to be fragile as it's made for quiet straightforward runs along city streets that only involve sweat, cold and light rain. On a challenging hiking trail involving thorns, rocks, stretching, mud, sleet and hours of continuous use, they'd fall apart.

And then there's the brands. In India, we mostly wore what we had lying around. My total annual clothing budget for hikes was zero. In the UK, my hiking trousers are from Craghoppers, my fleece from Pierre Cardin, my jackets from many assorted places, my hat and gaiters from Karrimor, my shoes from Crivit, my socks from Gelert. It will get worse if I do winter hikes. Other common brands are Merrell, Patagonia, Berghaus, Rab, The North Face, Regatta, Marmot, Paramo and Hi-tec. And then there's the walking poles, head torches and energy bars. You could spend hundreds of pounds a year on equipment.


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Sunday, 3 January 2016

On Null Hypothesis Significance Testing, P values and the Scientific Method


Hypothesis Testing 

Hypothesis testing is essential in science to determine the presence of an effect. A technique commonly used is NHST, which tests if the data points in your alternative distribution are representative of the normal distribution i.e if your data distribution is different from what would be considered 'normal', and assigning a p value to your data. If the Mean in your data sample is different from the one in the normal distribution, this might tell you that your data is not simply a random sample but that an effect (your variable) is present.

We conduct Hypothesis Testing by comparing our alternative hypothesis against a null hypothesis. You either reject or fail to reject the null hypothesis (double negatives can be used in statistics - not implausible, failed to reject, etc.).

Failing to reject the null hypothesis - This does not mean that the null hypothesis is true, only that this sample does not show that the alternative is true. Not rejecting a position like the null hypothesis does not mean that we're saying it is correct.

Rejecting the null hypothesis - This does not mean that the null hypothesis is false/not true. Neither does it mean that the alternative is true. It just means that this sample shows that the data is different from the null. Another sample might not. 

These two points above are important to understand because when we look for effects in data, particularly noisy data which might be influenced by a lot of factors, you cannot simply reduce the act of spotting an effect to rejecting or failing to reject a null hypothesis. This is because the null hypothesis is almost always false. 

When you sample from a population, it will be a coincidence indeed if you get the exact same means in both your experimental and control samples. I think the more important question to ask is how much of an effect is present and under what conditions will it vary. Statistics is no substitute for thinking. You need to decide what an important effect is. 

Other points to remember - 

- If you have a research question, circle around the problem, address it in different ways. Don't frame it in one specific manner and pin your conclusions on a null hypothesis to be tested.

- Hypothesis testing does not have to be applied to all questions. You can have one-off events worth studying that do not need falsification.

- It's OK to conceive your hypothesis after you have conducted research but it should be before you have analysed data statistically (more on this later).

- Hypothesis tests are always about population parameters, never about sample statistics. We always use the sample data to hypothesise about the population mean, not the sample mean.

- Hypothesis testing and significance testing are different things. Hypothesis testing or Null Hypothesis testing is about  rejecting or failing to reject a null hypothesis, Significance testing is about assigning a p value. We commonly use these two together in a hybrid called NHST, which is controversial.

Null Hypothesis Significance Testing (NHST) and P values

In order to conduct a hypothesis test, we usually assign a significance value, a threshold on which we decide whether to reject or fail to reject the null hypothesis. This is how the NHST methodology works, but it has drawbacks, like a dependance on the p value. A p value is supposed to quantify the strength of evidence against the null value. It tells you how unusual the occurrence would be if it was due to chance.

The p value is the probability of observing a sample statistic like the mean being at least as extreme/favourable as it is in this sample, given our assumptions of the population mean.

p value = P(sample mean being as extreme | assumption about population mean)

It is simply the probability distribution on a normal normalised distribution like a Z score table (you can find it using the pnorm function in R). For example if you test two groups of people and group A gets 5 and group B gets 7 and you want to see if their scores are significantly different from each other, you subtract the differences and get 2 and then decide if this is significantly different from your null value, whatever it is (probably 0), given a certain standard error (Remember that all statistics is essentially a test statistic divided by the error in that statistic). 

One way to do this is to be so immersed in your subject matter, be a complete expert at it and have full subjective contextual knowledge that you know subjectively if a difference of 2 really matters, if it really translates to real world significance. Remember that real world and statistical significance are two different things. 

In statistical significance, you would run your test statistic against a normalised distribution, assuming it follows one, and your data might just be deemed significant if you get a low p value. The low p value is supposed to tell you that the probability of getting this difference of 2 is low i.e on the lower end of one end of the normal distribution, given a null default. 

There are a few drawbacks to using p values as indications of significance. This paper shows us the harmful effects of using NHST and confusing statistical significance with real life significance but I've included my own notes below.

Significance testing tells you more about the quality of your study (variation and sample size) than about your effect size which is more important. Andy Field has written a very easy-to-follow chapter on this topic.

- As I said before, p values are the probability of observing what you observed given a null default, but the default is never null. The null hypothesis might always be false since two groups rarely have the same mean. How then do you make sense of how probable your data is?

- The p value is conditional on the null hypothesis. It is not a statement about underlying reality. Even if it is accurate, the p value is a statement about data when the null is true, it cannot be a statement about data when the null is false.

- A p value is not the probability of the null hypothesis being true or false. The p value is the probability of extreme data conditional on a null hypothesis. 

- It is not the probability of a hypothesis conditional on the data. P values tell us about our data based on assumptions of no effect, but we want a statement of hypotheses based on our data. To infer latter from a p value is to commit the logical fallacy of inverting conditionals. 

- P values do not tell you if the result you obtained was due to chance, they tell you if the result was consistent with being due to chance.

- p values do not tell you the probability of false positives. The sig level (not the p value) is the probability of the type I error rate i.e P(Type 1 error) or P(reject | H0 is true).

- This paper does a good job of expanding on my points above, listing a lot of the common misconceptions about p values and NHST. Highly recommended.

If you're studying a non-stable process that spits out random values, p values are not meaningful b/c they are path dependent. In these cases, the p value isn't meaningful b/c it is a summary of data that has not happened, under assumptions that further data will follow a certain distribution. 

- People use 0.05 as a significance level, but need to remember that hypothesis tests are designed to call a set of data sig. 5% of the time, even when the null is true.

- Many studies show that you have a a very good chance of getting a significant result that isn't really significant with a significance level of 0.05 (about 30% of the time). This paper in particular does a good job of explaining the high false discovery rate using a significance level of 0.05 and compares it to the screening problem, and this article summarises the points well. You can use a lower level like 0.001, but it really is up to you to decide what is statistically significant. 

The Scientific Method

All of this tells me that it is best, when tackling a solution to go back to the philosophical foundations of why we do things. 

Note that you only create a theory or hypothesis after you have evidence. Theories have to be based on evidence, preferably good data-driven evidence. You can't first make up a theory and then look for evidence to confirm or falsify your theory. This is how superstitions and pseudoscience are created. A deliberately vague theory will never be confirmed or falsified, only made to look unlikely. While quantifying how likely or unlikely the existence of an effect is, is the point of science, doing so is a waste of everyone's time if the effect was made up to begin with, so don't do this.

If you see something weird you can't explain, you don't automatically give it a name. That's merely classifying a phenomena, putting it in a box that represents what you already know of the universe, which is incomplete. And your classification system or model or framework could be wrong. You need to do more. It is best to sit on the fence, admit your ignorance, and keep exploring, digging and asking questions of your phenomena, all the while building better and better models to explain it and make predictions. This is preferable to classifying your phenomena in terms of some-pre existing narrative that fits your own socio-cultural context, which would be a failure of critical reasoning.

I see this all the time. Once people identify with a narrative, everything they see will serve to strengthen that narrative. Supporters of a political party do not support that party because the evidence led them to support that party, they do so because of other reasons, like values that they identify with. But once the decision is made, evidence doesn't matter. We are slaves to narratives. Everything that follows is confirmation bias.

We use models because of their usefulness, not because they are correct. It seems to me that the best way to tackle a scientific question or puzzle is to first do exploratory research, just lots of multiple comparisons, or A-B testing, and obviously we wouldn't use p values here. We look at our exploratory data, at possible trends we see and that might or might not be true, that might reflect some underlying connections, and then create hypotheses based on what we've found in the data. 

Here is where we switch from exploratory to confirmatory research. To confirm or falsify our hypotheses, we need to run experiments, which can involve hypothesis testing. And we have to gather new data for this. We cannot use the same data set for both exploratory and confirmatory research as that would be cheating ourselves and would not be scientific. 

We pre-register our experiments so we can't change our minds later and claim we were always looking for what we ended up finding. This is called p hacking - You can only test 1 hypothesis, not 20 and then report only 1. Or drop one condition so you get a sig. p value. There are really millions of variables that can correlate significantly with each other. Which is why we get significant correlations when we generate hundreds of 10 number strings of random numbers and then compare two strings. When you compare enough variables, you will find significant results. This is noise. This is just how large data works, or data without theory, or data with a theory that is ad hoc or made up and not evidence based. This is how superstition works. You need to look beyond this, to see if any of these correlations or effects are consistent and not merely noise.

So we conduct our confirmatory research, get our results, and then replicate to see if the results hold. Replication ensures that we confirm that the effect is real and wasn't just a coincidence.

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This then is 3 different experiments that we have conducted to find one effect. And where do p values come in? I think you can use them for confirmatory research, but only to tell you about your sample data distribution, about the probability that the data is consistent with chance, under repeated attempts. But you cannot use p values to tell you about your hypotheses. From what we've seen, p values cannot do that. They were not set up for that purpose and they don't work that way. You should be able to tell what a truly significant result is in your study without p values, or by looking at other statistics. Or maybe using Bayesian statistics.




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On Happiness


I've been thinking about happiness recently, which is probably something that someone who is truly happy wouldn't do. Happy people don't think about or look for happiness. They merely live out their happy lives as normal. But over-thinking things is part of who I am, and it brings me an extreme sense of satisfaction, which I suppose is different to happiness but still important.

I meet a lot of expats in London. International working professionals here on a contract. They all come here for a change, to lead a better life, to make more money, to travel and see new places, or other reasons that they claim brings happiness. And I wonder how many of them are happy. Whether this is a useful question to ask is something I'll get to later. But lets say it is. Lets say happiness is important. Do people who move here for work end up happier than they were in their own countries? I'm not sure. A lot of them feel like they're merely chasing happiness, like they're still searching for something that they'll never find, or that they've only found temporarily until another happiness goal catches their fancy. I'm not sure.

There's this TED talk that says that happiness is the mostly the quality of our relationships with other people, and I'm inclined to agree with this from the point of view of my own personal context. I personally derive a lot of happiness from good close personal relationships and shared experiences with family and friends, though I also think that other factors help - like having low expectations about certain things, having a pragmatic view about bad things that happen to you, having a positive attitude towards everything, and not tying your ambitions and career goals to happiness. Work for money, create for love, right?

This other talk separates happiness into synthetic and real. Synthetic happiness comes from doing what you are told will bring you happiness, accepting things you cannot change, and rationalising bad things as normal and happy. Also, people like things more when they think they're going to lose them. It defines real happiness as when we get whatever we want, which is something I don't get because we never get what we want and will constantly be striving from one happiness goal to another i.e one temporary island of happiness to another temporary island of happiness. It could just be semantics, but this isn't real happiness to me, this is just temporary contentment. But I guess this is happiness to a lot of people in the western world, who feel like they need to be in control of every aspect of their lives, and that control brings happiness. I take the other view, which is that since so much is out of your control, you can only be happy by letting go of it all and just do things you enjoy without hurting people, and take everything else in your stride without imagining that the universe is conspiring against you. Which is where the synthetic happiness come in. 

Then there's 'The Geography of Bliss' by Eric Weiner. A somewhat humorous look at why people in some countries are generally happier than others. Some of Weiner's book is of course typical western narrative tropes and hyperbole - Columbus, China's greed is bad, etc., but i picked up a lot of interesting points. Weiner visits the happiest countries on Earth  to find out what makes them happy, while not confusing correlation/association with causation. Just because happy nations are characterised by certain factors doesn't mean these are causal factors, it could be the other way around. 

The happy countries - 

- The Dutch have things taken care of, and have permissive attitudes towards sex, drugs, etc.

- The Swiss are less tolerant than the Dutch, they have rules, boredom and nature. They are not ecstatic joyful, but content. They also have cleanliness, punctuality, things taken care of, they don't provoke envy in others, but suppress envy by hiding their wealth. They are surrounded by beauty and nature. They trust their neighbours, and having a sense of history and where they're from. They have fewer choices.

- The Bhutanese don't have unrealistic expectations. They don't try to be happy or try to achieve it. They don't talk about or analyse it. They don't ask themselves if they will cease to be so. Ignorance is bliss. There is also a lot of death, which gives you a different perspective on life. You develop a new way of seeing things after living with it. They are poor, but that doesn't matter. Money is only a means to an end. It is trust in people and institutions. Material wealth doesn't become so important.

- The Qataris leave everything to God. Maybe happiness come from beliefs, not necessarily religious beliefs. They belong to one tribe with many rules, that allows you to have no rules outside it because you just won a lottery and can do anything with the money. You are happy as long as you are a high ranking member of this tribe. You don't need ambition or high expectations. The money takes care of everything. If this culture-less life is to your liking, you are happy. But money isn't everything - it has diminsihing returns. You will always crave somethign else.

- The Icelanders are naive. They are free to try and to fail. They have a conection to their language. They are a small country, feel kinship to each other, protective of their well-being. Enjoy writing. Not affected by SAD. Have multiple identities, no envy of others. Suppress envy by sharing everything with others. A sense of self actualisation and the freedom to do what you want. they are free to share ideas without copyright. Self-delusion might be good - there's no one to tell you not to do somethign or express yourself. They constantly fail and create rubbish, but are happy doing so.

- The Thais have mai pen lai (never mind), jai yen (let it go), sanuk (fun). They have fun at work instead of the American work hard, play hard mentality. Their fun is interspersed throughout the day rather than regimented and taken too seriously. They don't take things too seriously. They don't think about things like happiness to much. Ignorance is bliss? They smile a lot.

The unhappy countries - 

- The Moldovans have a lot of envy, are relatively poor compared to their European neighbours - poverty breeds envy of other's riches - there's also lack of trust - if something goes wrong, it is not their responsibility to fix. There's a feeling of powerlessness, helplessness.

The somewhat happy countries - 

- The British believe in muddling through, getting by. They are reserved, not tactless, are afraid of offending people, don't hug, are a country of grumps. Does culture impede happiness? I don't think it's that simple. Having lived in England and Scotland, I think people here are definitely happy, they just don't show it (btw, don't ever introduce yourself right away in an English pub - rookie mistake). But I'm not sure why they would rank lower than the other countries. 

- The Indians are a mixed lot. The ones who are happy believe that life in an act, and don't take it too seriously. New tech cities are both the problem and the solution. People have long long work hours, poor work life balance, and then special workshops and ashrams to fix them. Calcutta's poorer are happier than America's poor - stronger family ties? (Btw, flattery can get you an interview in India, and much else). He says nothing about unhappy people in India. I guess it could be a lack on trust in your neighbour and public institutions. All the happiest people I know in India derive happiness from relationships in their communities, but not necessarily within communities. Indian diversity can be comforting, but I think people's biases and ingroup-outgroup mentality combined with their narrow-mindedness about culture can serve to increase create distrust and hate.

- The Americans are constantly searching for happiness. Their unhappiness could come from unrealistic expectations. Self help books teach them to look inwards not outwards towards relationships that really matter. Maybe you nee to commit to a place or people to be happy, you can't always have one foot out the door.

What happiness isn't - 

To quote the book, "Happiness is not feeling like you need to be somewhere else or doing something else." But I think that's your other goals, which are fleeting and constantly changing. I think it's fine to have them, we all have career and self-fulfilment goals and wants, and striving to accomplish them is fine, but our success or failure in said exercise shouldn't make a difference to our happiness, if in fact happiness is more important. 

It's not about ambition or success. Failure might happen despite your best laid plans, and while success can bring you satisfaction, I feel it's the journey, the striving for success that brings you happiness.

Knowledge doesn't necessarily make you happier, though it has other obvious advantages. So is ignorance bliss? Not necessarily, in my opinion. It's not about knowledge vs ignorance w.r.t happiness. Neither is a factor, your happiness depends on other things.

It isn't about money or material wealth. Money helps, but just a little, it doesn't guarantee lasting happiness. Law of diminishing returns.

What happiness is -  

We constantly try to synthesise happiness, we think it is something to be found. Perhaps it is more a thing to be created, or a state to be evolved into. To my understanding, it is having close personal healthy relationships with friends and family, living in a society with a lot of trust, and no envy, uncertainty or fear, and finally, having a pragmatic outlook on life, understanding that events are unpredictable, but having something to look forward to and doing your best anyway, about having a sense of not-wanting. Living among a homogenous society with reliable public institutions and like-minded people also helps. The closer knit the community the happier you are, as long as you subscribe to the cultural mores of that community. Tough luck if you don't. Perhaps that's why people move away. To me, certain environmental conditions also matter, like living in clean cool quiet surroundings with access to good food and being intellectually stimulated.


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Monday, 21 December 2015

The Science of Everyday Thinking on EdX


I had the pleasure of completing 'The Science of Everyday Thinking' on EdX recently. The course deals with a lot of stuff i've been thinking about for the past few years, so I noted a lot of my thoughts.

Illusions

The course begins by stressing that it is really difficult to put yourself in the shoes of others. We over estimate the abilities of others to know what we know. An example of this is when we tap out a song on a table. We expect 25% of people to guess the song correctly but in reality only 2.5% do.

We're great at pattern recognition, maybe even too good at it. Things float to the top of our minds that match our expectations, so we see real effects in noisy data, for example -  a face on toast. We sharpen things to what we expect to see - the 'expectancy effect' - and level those that we don't.

The course also stresses on how faulty memory can be. Memory is not like a video camera. Every time we remember something we reconstruct past events in our mind. I have had personal experience with this when helping one of my classmates at uni with false memory experiments. It was interesting to see how people really believed that they had seen something when they hadn't. I do this to, which is why I now write down certain events immediately after they happen so I don't get sequences of events mixed up.

We exhibit Naive Realism - we think the world is as we perceive it to be. This is wrong.

We exhibit fundamental cognitive error - we tend to underestimate the contribution of our beliefs and theories to observation and judgement, and fail to realise how many other ways that they could have been interpreted. 

Know Yourself

Planning fallacy - we are terrible at planning or judgement-making or self-assessment. Examples are driving, attractiveness & morals. Even though we fall on a bell curve for some of these, and 50% of the population falls below the median, we are incapable of accepting that we could be in the bottom half. Statistically speaking we all have to be under 50% at some point, but we will never admit it.

I've seen this first hand when planning my own goals. Many a time, I've planned out a journey assuming I'd be ready by a certain time only to find I've taken longer to get ready. I overestimate my own ability to be ready in time. It's the same with my learning goals. I keep subscribing to the belief that I am a super-fast learner and can do multiple courses at once, and I always end up struggling with too many things on my plate. I've learned to cut back and take things slower. No one can be great at everything. I've also seen this when proof-reading for foreign students au University. Students would be incredulous at the number of mistakes I found in their writing and the amount of re-writing that was required. They thought their grammar was decent, when it wasn't. Their unrealistic expectations were tied to incorrect evaluations of their own abilities.

The false-consensus effect - we overestimate the extent to which our beliefs are typical of those of others. We believe that other people generally think like us. Important to be reminded that this is not the case.

People don't even know what makes them happy. The true reasons people are happy are usually different from the reasons they provide. I need to do a separate post of happiness as I'm currently researching this. 

Job interviews are usually bad because of confirmation bias - interviewers see what they expect to see. They make up their mind about a candidate soon after they meet them and then only ask questions that confirm their beliefs. Structured interviews, where every candidate is asked the same question, are better. 

People tend to exaggerate the long term emotion effects that events have on us. In reality, emotional trauma can have bad effects on us but for the most part we tend to over-emphasise their effects.

People have a strong 'order effect' when selecting from an identical pool - they mostly pick what's on the right. And then they don't believe the reason why -  which shows that we don't know ourselves well. We don't even know why we make certain choices.

Intuition and Rationality

Kahneman differentiates between System 1 and system 2 thinking i.e intuition and rational thought.

The Anchoring Effect is powerful - but be careful of noise in the data.

The Representativeness Heuristic - the frequency or likelihood of an event by the extent to which it resembles the typical case.

But from a practical point of view, do be careful of thinking too statistically - in the Rudy the farmer  example, where there are far more farmers than lawyers, statistically it would make sense to pick farmer as the option but a bit more context would propbably point towards one of the other options like lawyer.

Learning

I really enjoyed this part of the course as I could take away more from this part than any other. Keys to learning better are to - 

Distribute practice over time - spacing helps. 
Set calendar reminders.
Use Retrieval practice - instead of merely re-reading material, cover and try to recall it.
Learn by doing - practice and discuss the content.
Vary the settings in which learning takes place.
Relate learning to your everyday experiences.

An important thing to remember is to not mistake fluency with learning. If you're finding a new topic too easy, you're probably not learning it well enough. You only think you understand it.

Experiments

Beware the Gamblers Fallacy.

Apple's shuffle feature - people don't understand how randomisation works, Apple had to make their product less random so people would perceive it as being more random even though it wasn't.

Finding Things Out

Many phenomena are simply examples of Regression towards the Mean - things balance out. This is more apparent when there is more noise in measurement.

Also, Post hoc ergo propter hoc - we assume a causes b because b followed a. It's kind of like those other common biases that make us believe in superstitions, like correlation is not causation, or false premise reasoning, or circular reasoning.

Experiments show that for most competencies, there is no diff between large and small class sizes.

Six leads to opinion change -

What do you really believe anyway?
How well based is your belief?
How good is the evidence?
Does the evidence really contradict what you believe?
What would be enough to change your mind?
Is it worth finding out about?

Extraordinary Claims

There are multiple ways you can interpret things.

Question your intuitions and be willing to give them up.

People tend to accept information that is consistent with their pre-existing beliefs at face value, but critically scrutinise information that contradicts their beliefs.
Health Claims

Pseudo-scientists tend to make ambiguous statements that you can contort to your expectations.

The Placebo Effect can be a false positive response, but most are Regression to the Mean. People seek help when they are sickest.

The Availability Heuristic - if a treatment turned out negative, you would never hear about it. 

Like cures like - a diluted part of the disease can cure the disease - is a common false belief. 

Natural is not necessarily better - arsenic is not good for you, indoor plumbing is.

Clustered disease is possibly the availability heuristic. You're confusing normal randomness and noise for an actual effect. You need to create and test a hypothesis to determine if a true effect like cancer clusters exist in a population.

Always ask - what about the other 3 cells? Given that you can have true positives, true negatives, false positives and false negatives, always look at the costs and benefits of the two ways that you can be wrong.

Applied Claims

For example - facilitated communication, forensic science, conspiracy theories, gun laws, gay marriage, asylum seekers.

The Expectancy Effect affects interpretation of forensic evidence like DNA. Experts who expect or desire to see something see the evidence in ways that are consistent with what they want to see - this is in part helpful, but can be disastrous.

People tend to focus exclusively on what they consider to be the evidence.

Belief in conspiracy theories is mostly cherry picking information.

False consensus effect - everyone thinks that everyone agrees with them.

Exploiting the Situation

There is not much correlation between personality and cheating, it is more about the situation. Certain situations can encourage honesty. 

Social conformity, the bystander effect, attribution error.

We assume that the way we see the World is the only way to see the world and anyone else that sees it differently is wrong and we attribute it to their  education, personal biases, propaganda, lower intelligence.

Milgram experiment - authority factor, diffusion of responsibility factor, channel factor (increase in shocks in incremental steps), no clear exit.

Nudging changes the channel factors to induce behavioural change.

Putting it all together

Be aware of your intuitions.
Have a healthy skepticism.
Simulate your future desirable performance in the present.
Test hypotheses.
Pick a few areas where you want to change what you're doing w.r.t thinking and personal biases, and focus on those.
Just because something is portrayed confidently doesn't mean it's true.
Read.

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I really enjoyed the course. I initially felt that the instructors spent way too much time on discussing personal biases and our inability to be objective and accurate with our perceptions and beliefs, and that they were repeating these points through the first half of the course, but I see now how useful and essential this was. Indeed, only good can come from these constant reminders.

Throughout the course, I was reminded of the biases people use to justify their superstitions and irrational beliefs, and why they won't change their minds even after being presented with evidence. For some reason or another, people will believe what they want to believe, and then pick and choose evidence to confirm that belief. They will see patterns where there are none because that is what they would expect of that belief. It helps if the belief is vague to begin with. This makes it easier to confuse noise for a true effect. They will assume that everyone should think this way. They will not understand that everything they see and interpret this way can be interpreted in many different ways by different people. They will not accept that their beliefs are a result of critical reasoning flaws or cognitive biases, nor be willing to test and verify their beliefs experimentally.



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Wednesday, 22 April 2015

Learning by Context


Education is not the simple direct process people think it is.

When you give a kid 10 encyclopaedias, he/she doesn't learn them all by heart. People, including children learn and remember by context. By memorable information. 

I had over a dozen encyclopaedias and general knowledge books reference books when I was younger. I simply used look at the pictures and read a little of the text if the pictures looked interesting enough. That's a lesson for learning designers. Focus on visual information and cues as much as possible. As little text as possible.

But that's not all. You still have 500 pages in your book full of information that the reader really has no reason to be interested in. Which is why that information has to be put in context that's interesting to the reader. My books were full of information about dinosaurs, but I had no reason to remember this information until I began playing Top Trumps card games about dinosaurs. It was the same with animals, cars, bikes and football. Playing these card games gave me a reference point for these topics. Browsing through my humongous books now became a slightly more productive exercise, as I'd stop to read and learn a little more about dinosaurs, animals cars or bikes when a picture caught my eye.

This is why children need to be exposed to as many specific stimuli as possible. Let them develop their own likes and ideas. Then back these up with a lot of easy reference material. Both of these are essential for building knowledge. Just one is not enough. A person is more apt to learn about Ancient Greek mythology when they have watched a film or cartoon about it and then a book or Wikipedia. Wikipedia itself would be next to useless. Because there's no motivating factor in studying a lot of information. The film provides context, and reference points that the book builds on.


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Monday, 13 April 2015

Interesting Links


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Tuesday, 7 April 2015

Making Sense of the World


Imagine the following three possible frameworks or models used to make sense of the universe, this world, and all behaviour and actions.

One

This universe was created by a race of aliens from another universe or dimension. Perhaps they exist outside of space and time (whatever that means). They either created or monitor all activity on earth. Perhaps they have ways of understanding and following human desires, or perhaps they interfere in human activity, according to their whims and fancies, or maybe not.

Two

This universe is a function of the Matrix. It is an artificial construct, a virtual reality built by citizens of the future, human or robot, as a giant experiment or project of value. As such, our existence is really just our consciousness responding to whatever 'they' want us to see. Our bodies are either plugged into machines somewhere in the future, or we only exist in digital form.

Three

This universe was created by the Abrahamic God. He exists outside or space and time. He hears and sees everything. He chooses to answer or ignore prayer. He has a plan for everyone. His ways are mysterious.

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These are your three frameworks. I made up two of them, with inspiration from the media. The third is a commonly espoused belief system. Here's my question - what makes the third model any less ridiculous than the first two?

Granted, my description of all three were brief. But you are free to build on all three. If your goal in choosing a belief system, framework or world view is 'does this make sense?' or 'does this prove useful?' or 'does this explain everything in this world?', then all three frameworks are equally useful.

Take any physical law or phenomenon like gravity or electromagnetism. Take any biochemical unit or process like cell or an organ system. Take any aspect of human or animal behaviour like jealousy or altruism. Any of the three frameworks could be used to explain these facets of our world. They can all be used to explain economic productivity, evolution, tsunamis and genocide. 

All the three models are somewhat vague, and that proves advantageous. Being vague means the model explains more variation. The more specific the model is, the easier it is to undermine. You could explain away any bizarre phenomenon into the variation that the model accommodates. And since it accommodates everything in its vagueness, the model is never wrong.

Do you see the problem here? All three models cannot be correct. And these are just three. The number of models you could invent to explain variation in what you observe is infinite. How do you tell right from wrong?

'All models are wrong, some are useful'. We use models that useful to us. We don't really care how right they are. We use a model because we find it useful. Because it makes sense to us in our specific context. But if all three models are equally useful, then why prefer one over the other? Is it because only one is the product of historical thought and cultural evolution, and the other two are more recent and more clearly 'fake'?

We think prayer works because it works for us, and that's enough. We don't seem to care that historical data shows us that affliction and death rates for polio, smallpox and cancer have no relationship with prayer. That irrespective of how holy you were or how hard you prayed, if you had cancer in 1900, you would likely die. That cancer survival rates have more to do with the invention of surgery, radiation and chemotherapy than anything else.

Perhaps it is best to go beyond retrospective usefulness in picking the right model, seeing as how any speculative thought can account for all variation in observable phenomena. Perhaps we should stop asking 'how much variation does this model explain?' and instead ask 'how much predictive validity does this model have?'

Any made up theory can explain what you see around you. It doesn't matter if the model involves God, aliens, AI, time travelling robots, space monkeys, or a new scientific theory. All these models or frameworks can seem equally 'valid' or 'right' in that they have an explanation for everything, answers to all your questions.

To decide which model is right, or to create a better model, it seem much more intuitive to base that model only on the evidence you have, incomplete as it is, and then test it and continually modify it by making predictions, admitting all along the way that your model will always be imperfect and a work in progress.

p.s.

In 50 years, when we do develop a vaccine or cure for AIDS, the same people who call AIDS a punishment from God will be thanking God for answering their prayers and curing people. It's easy to validate any model using retrospective post-hoc rationalisation, especially if your model was vague to begin with, you never tested it by making specific predictions, and you're making it up as you go along, avoiding any attempt at testing your beliefs and instead picking and choosing facts that seem to fit into your pre-existing framework and ignoring everything else or considering it a test of your faith.

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Thursday, 2 April 2015

On Poverty


A lot of us don't realise this but it is really difficult to get out of poverty, even if you really want to. 

There are a lot of economically disadvantaged people out there who are smart and hardworking and have the means to remove themselves from poverty. And they do so very slowly. Because it's not an easy process. Let's say you work a double shift for six months and save up a bit of money, and then suddenly a family member gets sick, and of course they don't have health insurance because you can't afford the monthly payments. So all your savings are blown away. Or imagine you've been saving for six months and then just when you're going to use that money to get to the next stage, start a business, invest in something that will grow your money for you, you meet with an accident, or you're robbed, or you need to buy a new fridge or washing machine. Your savings are gone along with your plans. You're back to square one.

Because something bad always happens. This is what it's like to be poor. It's always one step forward, one step back, and so on. The rest of us don't experience this because we're rich, relatively speaking. We have a cushion. We have money in the bank that we can use to buy a new AC, fridge or washing machine. We always have enough money for healthcare. We have relatives we can borrow money from, networks of friends and ex-colleagues we can use to find a new job. We have cushions. The poor don't. These cushions serve to keep us from stumbling, they keep our careers moving forward and not grinding to a halt every time something bad happens. This is why I don't get how people look down on the poor as if it's their fault, like they are lazy. They aren't. No one likes being poor. They're working to get out of it. You just can't see it because of your privilege.

So how do you get rid of poverty? You could increase wages. Imagine a janitor in Sweden. He gets a minimum wage that's enough to afford a home. He's not rich, but he makes ends meet. Same with the UK or US. Now imagine a janitor in Mumbai. His wage, even if above minimum, would be nowhere near enough to rent a flat. So he lives in a slum. He saves more that way. Could the government enforce a minimum wage that's high enough so everyone can afford proper housing and not live in a slum? Sure, but employers would pass that burden back to customers. We would eventually pay more for items, and would want higher pay ourselves to cover the difference. Which isn't a bad thing. We would all earn more, and pay more more some things. For people doing menial work, their savings would be low but their living conditions would be decent. The rest of us would have higher pay and higher expenditure and our savings would be proportional. More importantly, we would all be living in a country with a higher standard of living, and no slums.

Or we could just leave it all up to market forces. The problem with this is that in a country with fewer opportunities, and less competition, employers can pay as little as they want, if they know there aren't any alternatives for you. They can always claim that people are free not to work for them if they find the salaries too low. This might be fair to the employer, but not to workers, because they live in a country with few opportunities by default, so they really have no where else to go to, and they can't all start their own businesses overnight because they are mostly disadvantaged to begin with. So they settle with being exploited because some pay is better than no pay. 

That's how rich people like Donald Trump end up legally getting even richer by building large buildings in the Middle East using voluntary 'slave labour', people who are too poor to do anything else and who aren't even allowed to keep their passports. Is it their fault their country didn't give them enough opportunity? Is it their own fault they were not smart enough to get rich on their own?

If you want to live in a developed country you need to remove absolute poverty. Relative poverty will always exist in a capitalist system, and that's OK as long as inequalities don't create further absolute poverty or lead to monopolies that create status quo institutions that can lead to exploitation. You could remove poverty by raising the minimum wage, ensuring that everyone has a liveable income. This by itself will only do some good. In Indian cities like Mumbai, it will enable to people to live in better places, or let them grow their savings. 

The government could just subsidise education completely of course. It already does that to a large extent. But that won't cure poverty on its own. If you waved a magic want and gave every Indian a PhD tomorrow, they still wouldn't have the ecosystem to use their skills. There would still be massive unemployment. You can't stop at education. You also need an environment that demands new skills, that serves as a market for these skills, so people can exchange their skills for money. 

They would also need a market that enables them to finance themselves and create their products easily. You could reduce bureaucratic procedures and other red tape involved in growing businesses, and incentivise patents and loans, to encourage self-employment and innovation. In the long term, this would create more jobs, and in turn serve as a motivator for people to up skill themselves, which would get them higher salaries, and better lifestyles. 


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