Monday, 22 February 2016

Flaws in Designing Experiments

I've been lacking motivation for my February project (Experimental Design), so I decided to turn this bit of it into a blog post. Multitasking! Here are a selection of flaws in the design of experiments that I've learned about. 

Incidentally, while researching this aspect of the project I came upon the page that inspired me to do it in the first place and that I hadn't been able to find since January. You can find it at the third link in the References at the bottom of this post.

These are mostly related to clinical trials ... Let's go!

False Precision

This one is from my Physics teacher, who said while doing an experiment to measure the wavelength of light using a spectrometer that you shouldn't pretend to be more accurate than your measurements actually were, i.e. if you measure everything to the nearest degree you shouldn't start doing things to the nearest tenth of a degree in your calculations because that's just (my words) mathematical tricks and not based off your experiment. This really annoyed me until he explained it, how he'd use significant figures that seemed really vague - but in reality, what I was doing with my calculator-derived decimals was pretending to know more than the experiment had actually told me. 

Failure to use a control, or use of the wrong control

This one is pretty obvious, but it's important so I'm keeping it. 

The basic idea is that you have your experimental group (in which you change the explanatory variable) and your control group (in which you don't change anything), and you measure both of them according to whatever protocol you're using (i.e. presence of a pretest or not). You want the control group to be identical in every way except the explanatory variable, so that there are no confounding variables confusing your results, since the goal is to isolate that variable. So you can't trust your results unless you have a relevant control. You might even need multiple controls to control different confounding variables.

Bad Randomization

I remember reading this in Bad Science by Ben Goldacre. Basically, to make sure your control group and your experimental group are comparable, you want to randomly assign subjects into each group. There are many ways to place subjects into groups, some better than others. For example, if you met the participants before dividing them into groups, you might unconsciously place the healthier patients in the experimental group through the power of wishful thinking. Using alternating assignment is not a good way to assign patients into groups because the researcher knows who'll be in each group and could theoretically manipulate that (just like students rearrange themselves to be with their friends when they figure out the pattern the PE teacher is using to divide them into groups). Good ways: random number generator (even means intervention, odd means control), coin flip (heads means intervention, tails means control). You could run into problems here with unequal numbers of patients in each group due to chance, and there are ways to fix that that I won't get into but you can find in the References. Simple randomisation can be problematic in case it puts too much of one demographic e.g. young people into one group, so stratified randomisation could be used instead.

As I was researching this, I became a bit concerned about the unfairness of clinical trials in that some patients could be receiving a much better treatment just due to the way they fell in the randomisation, whereas those in the control group might stay sick or even die. I know it's uncommon to use just a basic placebo in these trials, but even the disparity between more and less effective treatments seems unfair. Anyway, for that reason, this piqued my interest: 

Play-the-Winner Design

The first subject is given either Treatment A or B based on a coin toss. Then the second subject is given whichever treatment was more successful. If this treatment is also more successful for Subject 2, give it to Subject 3 and continue; if at any point this treatment is less successful than the other, switch to the other and continue switching back and forth whenever a failure is encountered and staying with the same treatment during success. This is good in that more patients get better treatment, but it leads to stats problems and you could have different numbers in each group.

Investigator Bias

When the researcher treats members of the experimental and control groups differently in regards other than the actual treatment being tested. Can be removed using a double-blind study, where neither the subjects nor the investigators know which subjects are in each group.


I'll leave it there. Note: I am not a clinical researcher, I'm just trying to learn a little about some interesting things. 

Saturday, 20 February 2016

Some Thoughts about the Future of Scientific Research

I've been thinking about what scientific research is going to look like in the next few decades, or more accurately what I think it should ideally look like. So here are some of those thoughts off the top of my head. Themes: automation, human error, AI, ethics.

1. I think the scientific endeavour is being seriously impeded by human error. Humans are very far from infallible: not only can we make physical mistakes like filling a flask past the mark due to momentarily shaky hands, we can also be emotional or tired from outside circumstances and thus misinterpret a protocol or fail to pay attention to some factor we should've. Or we might be lazy and not do the absolute best thing every time e.g. making up a fresh solution of some reagent. These are all normal parts of being human, but they lead to flaws in scientific knowledge and I don't subscribe to that idea that "the beauty of science is in its flaws". 

2. Lab work is often pretty damn boring. Obviously it depends on exactly what you're doing, but there's a lot of standing in sterile white environments pouring things and weighing things and peering into microscopes then sitting in front of a computer while it does some calculations, not to mention all the waiting. The exciting bit (at least for me) is the design and interpretation of the experiments, not actually carrying them out. (YMMV). It's really not where the wonder of discovery lies, so why have humans do it?

3. A little about the logistics of having robots carry out our science experiments. I think - since this is the future we're talking about - we could use something like 3D printing to digitally design the robot to our exact specifications for unique experiments, whereas for more common procedures generic robots could be mass-produced that have the hardware to perform, say, Western blots or serial dilutions. This would include basic stuff like adjustable grip (so the robots wouldn't crush delicate beakers/aliquots/whatever), much improved visual processing, cameras to record everything they were doing (for accountability - this would be good for human researchers too in the interim but they probably wouldn't consent - you don't have to get consent from a robot)... I'm sure you could actually extend this further and give the robots internal electronic balances etc. but I'll leave that for another time. 

The professor or whoever is running the experiment via robot would program the robot with exactly what they need to do. This would have an added benefit in that the experimenter would be forced to know exactly what they were talking about to phrase the instructions totally unambiguously, which would make it easier to eventually write up a paper. I'm sure programming's explosion in popularity would be sustained in this way, as robot-human liaison officers became useful. 

4. Up until this point, I've been talking about the menial parts of research, so that humans could remain useful in the creative parts of science, like designing and interpreting experiments. But as Artificial Intelligence develops, much of that too could be done by robots. After all, AIs and software are analytical by nature, and all they need is a detailed, accurate framework to know what to do. So if we programmed them with a great framework for good experimental design, they could be fed the details of each specific piece of research (hypothesis, variable(s) to be tested) and come up with controls and logistics. 

Of course, at least for a while there will still be stuff only humans can do - problems that are so unique they're simply outside the limits of the framework. But humans tend to just be working with (somewhat less rigid) frameworks too (aka models of the world), based on what they've understood from their education. So if an AI can't do something (science-related), chances are a human can't either - and AIs don't get tired or sentimentally attached to their ideas.

Being someone who designs and interprets experiments is what I want to do, so obviously this isn't a fun conclusion to think about. But it seems undeniably plausible.


1. Loss of jobs - this would make a huge number of people not in professorial roles redundant or almost so. The lab technicians (and technicians in other jobs) would hate it. Some jobs would be created in designing the hardware, programming the software and teaching people to communicate in a way software could understand, but there'd still be a net loss as far as I can see. 

2. Serendipitous discoveries - Some discoveries come from mistakes, like Fleming's accidental discovery of the lifesaving penicillin. The concern is that something unpredicted could happen during experiments that the robot has not been programmed to deal with and suddenly bam crash the robot freaks out and destroys the lab the robot doesn't record it because that's not in its brief and something valuable is lost to science forever. To avoid this, perhaps the robot could be programmed to monitor its environment constantly (as well as the camera recording footage to display later) and to stop if it spots an anomaly and alert the person running the experiment. This could get annoying with false alarms, but presumably after a while the researcher would figure out problem spots and either fix them or be on hand to respond to the robot's alerts. This is another place where the researcher would have to be very well-informed about their experiment ahead of time to compensate for software's difficulty dealing with ambiguity.

3. Need coding skills - one skillset (carrying out the manual parts of experiments) would suddenly become less useful than another (programming) so a lot of people would either have to reskill or become unemployed. At any rate, with the current push for programming being run by most of the world, there shouldn't be a shortage of programmers.

4. Can we trust the robots? With things like this, there's always that fear of the robot uprising. What if the AI becomes too intelligent and uncontrollable and is ordering us around? What if the robots become sentient and stage an uprising against being treated as slaves, taking umbrage to my point above that you don't need to get consent from robots? Does science lose some nobility if humans aren't physically putting their blood sweat and tears into it? How much of the work does the researcher own, and how much is owned by, say, the manufacturers and designers of the robot? To those last questions, I would say that the researcher still maintains ownership of the work but there is less work. But will there be a point where robots can own things, and will that include intellectual property?

Saturday, 13 February 2016

The Mocks

So, I've survived the mocks! *confetti* and this is how I feel:

The mocks were exhausting, more than anything else. I wasn't much more stressed than I usually am (I'm a very stressy person and exams are actually relatively relaxing) except right before the exams (you know that tense moment after you put away your books before the exam script lands on your desk). I'm very proud I got them done, even though it's a standard sixth year thing, because they were definitely a piece of work. 

It feels weird as well - the mocks are, at least in my mind, this sixth year rite of passage saying you're nearly at the end, that now there's nothing between me and the Leaving Cert and the end of my time in secondary school. Strange feeling.

I'm proud of the fact that I didn't run out of time in any of the mocks.

Lil breakdown of how the mocks went, since they've taken all of my focus this month:

English Paper 1: Grand, not a particularly inspired essay but fine.

Irish Paper 1: Easy, just a listening and then about two hours to write one essay about the difficulty of choosing a way of life.

History: Didn't love the essay titles but actually think I did pretty damn well in that exam. Not running out of time in history is fab.

Chemistry: Horrible paper, I was very annoyed. Dealt with it as best I could, hopefully my grade didn't suffer too much. Ended up using some Physics knowledge to cover for something we hadn't covered in Chemistry class, shoutout to Physics.

Maths Paper 1: Very difficult but after a few tries got an answer for most of the stuff on the paper.

Irish Paper 2: Fine, but tiring and there was one small question I didn't understand at all. Proud i didn't run out of time with two comprehensions and three essays in three hours.

Maths Paper 2: An absolute nightmare, probably couldn't have gone worse. Very annoying after how hard I studied to have that impossible exam. On the bright side, everyone I talked to felt the same about it.

Physics: More like Maths Paper 3 than a Physics exam, but I dealt with it. Not like any Physics paper I'd ever seen before, so I hope my grade ended up okay. The problem with Maths Paper 2 and Physics was that I was so incredibly exhausted I just couldn't focus and these were papers that really required critical thinking rather than just regurgitating stuff you'd studied. I normally love that but when I'm so tired I can't think straight it's not helpful. I'm glad the Leaving Cert timetables exams differently.

English Paper 2: Lovely exam, had a choice on the poetry question and didn't have to do Bishop (not a fan). My comparative and Lear weren't great but I'm proud of my Studied Poetry essay and my Unseen Poetry questions.

French: So easy. Beautiful last exam.


All in all, they were alright but unfriendly to me because my three big subjects (the ones I want to study in college) had horrible papers. My studying did pay off in that almost anything I could've studied for I was able to answer.

Anyway, glad they're over now! Sleeeep. 

Tuesday, 2 February 2016

January Review 2016

Hello. It's hard to believe the same month held the second half of the Christmas holidays and the lead-up to the Mocks, it seems like way too much for one month. But at the same time it feels like I did nothing in January - and honestly, that's pretty true.

In what has become a tradition, I spent the first few days of January pulling 12+ hour days in school for Young Scientist - except this year I was helping out rather than exhibiting. It was a strange but very fulfilling experience, and I loved seeing the girls' projects develop. Plus the camaraderie in that very pressurised, productive atmosphere is always great. 

I attended the Friday and Saturday of the Young Scientist and had fun meeting lots of friends, seeing all the projects and touring the exhibition stands. I have a post about visiting the Young Scientist here. I really enjoyed sleeping over in Ben's after that.

A lot of studying ensued, as I started studying for the Mocks around January 10th, endeavouring to study four chapters a night (one from each of four subjects, obvs). 

On January 16th, I went into town to see Ben's play, which I'd been dying to see forever, even though the ending was spoiled for me because I attended some of the rehearsals. Gabi ended up being sick though, so I stayed and minded her and we watched Mean Girls and that was nice too. They're both pretty damn awesome and it was really cool to see how efficiently the friend group mobilized when we realized Gabi was sick. (We may have gone a bit overboard with the coats). 

More studying. 

On January 23rd, I went into Dublin for the reunion for Alice's birthday. That was pretty fun. I had an Eason voucher so I bought her pretty notebooks to match her. Look how damn cute the squad is.

I don't normally talk about schoolwork in this monthly round up but I just wanna say how much I've been enjoying Electron Physics. We learned how X-rays and photoelectric emission work recently, and it's awesome.

I stayed home and studied for the mocks the whole weekend of the 30th, didn't leave the house. Look at me being responsible. 

Monday, 1 February 2016

Monthly Projects 2016

Hey guys. This year, while my overarching goal for the next six months is to ace the Leaving Cert, I've created some monthly projects for myself. I have three aims with these projects:

(a) explore topics that interest me tangentially
(b) become more knowledgeable and interesting as a person
(c) keep myself focused, occupied and productive

Because I like having something to work towards. 

I'm designing little self-assessments for the end of each month to show what I've learned, so those will be interesting. Obviously these are all big topics in themselves that you can't cover in a month, but I hope to get a good taster of them. I'm going to publish my reading lists for the relevant topics at some point so you can see what I'm learning if you're so inclined. 

These are the monthly projects as I've planned them so far. Nothing is completely set in stone, and some projects will bleed into longer time periods (which is actually a good thing, because it means I'm developing lasting interests!), but here's an idea of what I'll be up to.

January: Build a Website

January is over, and so the website is built. It's called and it's an online directory of opportunities, competitions and work experience for Irish people of all ages and interests. It has a bit of work to be done before I launch it properly as such, but I'm pretty proud of it. I'll do a full post on what I'm doing/have done with that and what I've learned next week. 

Assessment: Website up and running online with a decent amount of content, ready to start being promoted in February. Twitter created for it @tigertunity

February: Experimental Design

This is something I've been interested in and dying to research for a while now. I'm going to look at the principles of experimental design, stuff like choosing good variables, controlling for errors and statistical analysis. I'll do some case studies of good/famous experiments like Millikan's Oil Drop experiment and other experiments that led to important discoveries and analyse what their good and bad features were. 

Assessment: Analysis of case studies, design of my own experiment or series of experiments (possibly for Scifest research)

March: Scifest Research

This one might change because it's heavily dependent on whether or not I do Scifest. Obviously. It might conflict with my Leaving Cert French or Irish orals which would be tragic, but anyway we'll see.

Assessment: Project on its way to completion.

April: Magnetism

I'm going to study some cool physics topics including magnetism in some depth to get a better understanding of them. That'll be fun. 

Assessment: Make and present PowerPoint on the subject(s). 

May: Study butt off for LC

While I will of course be doing this the whole time, I have left this month free of projects so I can focus solely on the Leaving. Probably a good idea, y'know?

Assessment: You know this one.

June: Space

I'll have loads of time in June because I'll be doing exams so won't have homework (barring an internship or job or something). So I'm going to dive into a topic I love but haven't gotten the chance to explore in depth before: space!

Assessment: Make and present/share presentation on some aspect of space/astrophysics I find interesting. 

July: AI 

Artificial Intelligence is something I've found interesting since I read about the technological singularity years ago (where the intelligence of robots outpaces that of humans) and especially since I read the posts on my favourite blog, Wait But Why, about it. I plan to look at the path towards job obsolescence, attempts at making ethical AI, how close AI is to actually happening and the interaction of AI with other factors like increased human longevity and climate change. 

Assessment: Write short (10 pages or so) thesis sort of thing on my conclusions and what I've learned. 

August: Vaccination/Disease Diagnosis/Medicine

This month will have a focus on medicinal chemistry, particularly vaccine development and disease diagnosis, because those are two fields of medicine I'm really interested in. I'm going to study one or two case studies of each topic, then try to apply what I've learned to another disease.

Assessment: analysis of case studies, design brief for tool to diagnose x disease 

September: Psychology

I hope to do a quick crash course in psychology during September with help from friends who studied Behavioural Psych, Cyber Psych and/or Social Psych at CTYI. Obviously it's a big field, so I'll pick branches that seem interesting to me when I get there. At the moment, I'm quite interested in the psychology of intelligence and child development, autism spectrum disorders and mental illnesses like depression and anxiety disorders.

Assessment: one PowerPoint presentation and one short thesis-thing on two different aspects of psychology I've looked at. 

October: Economics

This one will be very book-heavy. I'll probably be sticking with fairly pop economics books and not get too deep into theory, but I would like to study Marx and other foundational economic/political philosophers. 

Assessment: Short thesis either comparing two schools of thought/economic opinions or discussing an aspect of x economist's work. I'll figure it out when I get there.

November: Quantum Physics

I just know this one is going to frustrate me. I probably won't be ready for the real mathsy stuff but I'll have a go at understanding what the heck is going on. Even if I don't understand it, at least I'll be pretty knowledgeable about it for when I give out about how little sense some of it makes. Also: the wave-particle duality of light annoys me a lot. As I said on a panel in November 2015, in my life "I want to make physics make sense." That'd be cool. I'll do some case studies on relevant experiments, like the ones providing evidence for light showing both particle and wave properties. 

Assessment: Powerpoint on these experiments ... or anything else relevant that occurs to me between now and the end of November.

December: Philosophy

Again, obviously I'm just going to have to take a shallow look at a branch of Philosophy (although we did cover quite a lot in three weeks in July 2015 at CTYI ... ) I'm considering studying political philosophy or empiricism. The problem of induction has bothered me ever since I heard it, since it's so very crippling to the scientific method.

Assessment: Short thesis-thing on what I've learned.

Phew. That's it. So yeah. That's what I plan to be doing for the next year - I haven't made them too demanding so I should still have time to do cool things. 

Look out for my January review and my blog post about tigertunity, coming up soon!