How to Ask A Good Scientific Question

One of the first tasks a scientist or curious person must undertake before experimentation is the formulation and positing of a scientific question. A scientific question is an extremely narrow question about reality which can be answered directly and specifically by data. Scientists pose scientific questions about obscure aspects of reality with the intent of discovering the answer via experimentation. After experimentation, the results of the experiment are compared with their most current explanation of reality, which will then be adjusted if necessary. In the laboratory, the original scientific question will likely take many complicated experiments and deep attention paid before it is answered.

For everyone else, the scientific question and experimental response is much more rudimentary: if you have ever wondered what the weather was like and then stepped outside to see for yourself, you have asked a very simple and broad scientific question and followed up with an equally simple experiment. Experiments render data, which is used to adjust the hypothesis, the working model that explains reality:  upon stepping outside, you may realize that it is cold, which supports your conception of the current time being winter.

Of course, a truly scientific hypothesis will seek to explain the ultimate cause as well as the proximate cause, but we’ll get into what that means later. For now, let’s investigate the concept of the hypothesis a little bit more so that we can understand the role of the scientific question a bit better.

Informally, we all carry countless hypotheses around in our head, though we don’t call them that and almost never consider them as models of reality that are informed by experimentation because of how natural the scientific process is to us. The hypotheses we are most familiar with are not even mentioned explicitly, though we rely on them deeply; our internal model of the world states that if we drop something, it will fall.

This simple hypothesis was likely formed early on in childhood, and was found to be correct over the course of many impromptu experiments where items were dropped and then were observed to fall. When our hypotheses are proven wrong by experimentation, our response is surprise, followed by a revision of the hypothesis in a way that accounts for the exception. Science at its most abstract is the continual revision of hypotheses after encountering surprising data points.

If we drop a tennis ball onto a hard floor, it will fall– then bounce back upward, gently violating our hypothesis that things will fall when dropped. Broadly speaking, our model of reality is still correct: the tennis ball does indeed fall when dropped, but we failed to account for the ball bouncing back upward, so we have to revise our hypothesis to explain the bounce. Once we have dropped the tennis ball a few more times to ensure that the first time was not a fluke, we may then adjust our hypothesis to include the possibility that some items, such as tennis balls, will bounce back up before falling again.

Of course, this hypothesis adjustment regarding tennis balls is quite naive, as it assigns the property of bouncing to certain objects rather than to a generalized phenomena of object motion and collision. The ultimate objective of the scientific process is to resolve vague hypotheses into perfect models of the world which can account for every possible state of affairs.

Hypotheses are vague and broad when first formed. Violations of the broad statements allow for clarification of the hypothesis and add detail to the model. As experiments continue to fill in the details of the hypothesis, our knowledge of reality deepens. Once our understanding of reality reaches a high enough level, we can propose matured hypotheses that can actually predict the way that reality will behave under certain conditions– this is one of the holy grails of scientific inquiry. Importantly, a prediction about the state of reality is just another type of scientific question. There is a critical caveat which I have not yet discussed, however.

Hypotheses must be testable by experimentation in order to be scientific. We will also say that hypotheses must be falsifiable. If the hypothesis states that the tennis ball bounces because of magic, it is not scientific or scientifically useful because there is no conceivable experiment which will tell us that “magic” is not the cause. We cannot interrogate more detail out of the concept of “magic” because it is immutable and mysterious by default.

Rather than filling in holes in our understanding of why tennis balls bounce, introducing the concept of magic as an explanation merely forces us to re-state the original question, “how does a tennis ball bouncing work?” In other words, introducing the concept of “magic” does not help us to add details which explain the phenomena of tennis balls bouncing, and ends up returning us to a search for more details. In general, hypotheses are better served by only introducing new concepts or terminology when necessary to label the relation of previously established data points to each other. The same could be said for the coining of a new term.

Now that we are on the same page regarding the purpose of scientific questions– adding detail to hypotheses by testing their statements– we can get into the guts of actually posing them. It’s okay if the scientific question is broad at first, so long as increasing levels of understanding allow for more specific inquiry. The best way to practice asking a basic scientific question is to imagine a physical phenomenon that fascinates you, then ask how it works and why. Answering the scientific question “why” is usually performed by catching up with previously performed research. Answering “how” will likely involve the same, although it may encounter the limit of human knowledge and require new experimentation to know definitively. I am fascinated by my dog’s penchant for heavily shedding hair. Why does my dog shed so much hair, and how does she know when to shed?

There are actually a number of scientific questions here, and we must isolate them from each other and identify the most abstract question we have first. We look for the most abstract question first in order to give a sort of conceptual location for our inquiry; once we know what the largest headline of our topic is, we know where on the paper we can try to squint and resolve the fine print. In actual practice, finding the most abstract question directs us to the proper body of already performed research.

Our most abstract question will always start with “why”. Answering “why” will always require a more comprehensive understanding of general instances that govern the phenomena in question, whereas “what” or “how” typically refers to an understanding that is limited to a fewer instances. So, our most abstract question here is, “Why does my dog shed so much?”

A complete scientific explanation of why the dog sheds will include a subsection which describes how the dog knows when to shed. Generally speaking, asking “why” brings you to the larger  and more comprehensively established hypothesis, whereas asking “how” brings you to the more narrow, less detailed, and more mechanistic hypothesis. Answering new questions of “why” in a scientific fashion will require answering many questions of “how” and synthesizing the results. When our previously held understanding of why is completely up-ended by some new explanation of how, we call it a scientific revolution.

At this point in human history, for every question we can have about the physical world, there is already a general hypothesis which our scientific questions will fall under. This is why it is important to orient our more specific scientific questions of “how” properly; we don’t want to be looking for our answer in the wrong place. In this case, we can say that dogs shed in order to regulate their temperature.

Temperature regulation is an already established general hypothesis which falls under the even more general hypothesis of homeostasis. So, when we ask how does the dog know when to shed, we understand that whatever the mechanistic details may be, the result of the sum of these details will be homeostasis of the dog via regulated temperature.

Understanding the integration between scientific whys and hows is a core concept in asking a good scientific question. Now that we have clarified the general “why” by catching up with previously established research, let’s think about our question of “how” for a moment. What level of detail are we looking for? Do we want to know about the hair shedding of dogs at the molecular level, the population level, or something in between? Once we decide, we should clarify our question accordingly to ensure that we conduct the proper experiment or look for the proper information.

When we clarify our scientific question, we need to phrase it in a way such that the information we are asking for is specific. A good way of doing this is simply rephrasing the question to ask for detailed information. Instead of asking, “how does the dog know when to shed”, ask, “what is the mechanism that causes dogs to shed at some times and not others.”

Asking for the mechanism means that you are asking for a detailed factual account. Indicating that you are interested in the aspect of the mechanism that makes dogs shed at some times but not other times clarifies the exact aspect of the mechanism of shedding that you are interested in. Asking “what is” can be the more precise way of asking “how.”

The question of the mechanism of shedding timing would be resolved even further into even more specific questions of sub-mechanisms if we were in the laboratory. Typically, scientific questions beget more scientific questions as details are uncovered by experiments which attempt to answer the original question.

As it turns out, we know from previous research that dog shedding periods are regulated by day length, which influences melatonin levels, which influences the hair growth cycle. Keen observers will note that there are many unstated scientific questions which filled in the details where I simplified using the word “influences”.

Now that you have an example of how to work through a proper scientific question from hypothesis to request for details, try it out for yourself. Asking a chain of scientific questions and researching the answers is one of the best ways to develop a sense of wonder for the complexity of our universe!

I hope you enjoyed this article, I’ve wanted to get these thoughts onto paper for quite a long time, and I assume I’ll revisit various portions of this piece later on because of how critical it is. If you want more content like this, check out my Twitter @cryoshon and my Patreon!

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How to Become a Smarty Pants

There’s been a small amount of interest that I’ve seen in a few communities regarding building status as an “intellectual” in the colloquial sense, and I think it’s probably more correct to say that people would rather be perceived as smart than as dumb, which is completely fair.

This article could also be called “How to Look and Sound Like an Intellectual” although frankly that implies a scope that is much larger than anything I could discuss. So, we have a lighthearted article which purports to transform regular schlubs into smarty pants, if not genuinely smart people. If you already fashion yourself as a smarty pants, read on– I know you’re already into the idea of growing your capacities further. Hopefully my prescription won’t be too harsh for any given person to follow if they desire.

While it seems a bit backward to me to desire a socially assigned label rather than the concrete skills which cause people to give that label to others, building a curriculum  for being a smarty pants seems like an interesting challenge to me, so I’ll give it a shot. I hope that this will be a practice guide on how to not only seem smarter, but actually to think smarter and maybe even behave smarter. The general idea I’m going to hammer out here is that becoming an intellectual is merely a constant habit of stashing knowledge and cognitive tools. The contents of the stash are subject to compound interest as bridges between concepts are built and strengthened over time.

In many ways, I think that being a smarty pants is related with being a well rounded person in general. The primary difference between being seen as an intellectual and seen as a well rounded person is one of expertise. The expertise of an intellectual is building “intellect”, which is an amorphously defined faculty which lends itself to making witty rejoinders and authoritative-sounding commentary. There’s more to being a smarty pants than puns and convincing rhetoric, though: smarty pants everywhere have been utilizing obscure namedropping since the dawn of society. Playtime is over now, though. How the heck does a person become a smarty pants instead of merely pretending to be like one?

Being a smarty pants is a habit of prioritizing acquisition of deep knowledge over superficial knowledge. Were you taught the theory of evolution in school? Recall the image that is most commonly associated with evolution. You probably picked the monkey gradually becoming a walking man, which is wrong. The superficial knowledge of the idea that humans and monkeys had a common ancestor is extremely common, but the deeper knowledge is that taxonomically, evolution behaves like a branched tree rather than a series of points along a line.

See how I just scored some smarty pants points by taking a superficial idea and clarifying it with detailed evidence which is more accurate? That’s a core smarty pants technique, and it’s only possible if you have deep knowledge in the first place. Another smarty pants technique is anticipating misconceptions before they occur, and clearing them up preemptively. How should you acquire deep knowledge, though?

Stop watching “the news”, TV, movies, cat videos, and “shows”. Harsh, I know– but this step is completely necessary until a person has rooted themselves in being a smarty pants. This media is intended to prime you for certain behaviors and thoughts, occupy your time outside of work, and provide a sensation of entertainment rather than enriching your mind. The more you consume these media, the less your mind is your own, and the more your mind is merely a collection of tropes placed there by someone else. Choosing to be a smarty pants is the same as choosing isolation from the noise of the irrelevant.

For the most part, these media are sources of superficial information and never deep information. You can’t be a smarty pants if you’re only loaded with Big Bang Theory quotes, because being a smarty pants means knowing things that other people don’t know and synthesizing concepts together in ways that other people wouldn’t or couldn’t. There is zero mental effort involved in consuming the vast majority of these media, even the purported “educational” shows and documentaries which are largely vapid. Seeing a documentary is only the barest introduction to a topic. Intellectuals read, then think, then repeat.

I guess I’ve said some pretty radical things here, but try going back and viewing some media in the light I’ve cast it in. There are exceptions to the rule here, of course: The Wire, The Deer Hunter, American Beauty, or an exceptionally crafted documentary. The idea is that these deeper works are mentally participatory rather than passively consumed; the depth and emotionality that the best audiovisual media convey can be considered fine art, and smarty pants love fine art. During your smarty pants training, I would still avoid all of the above, though. Speaking of your smart pants training…

Stop reading “the news”, gossip of any kind, Facebook, Twitter, clickbait articles, and magazines.  These things are all motherlodes of superficial information. As Murakami said truthfully, “If you only read the books that everyone else is reading, you can only think what everyone else is thinking.” This concept is absolutely critical because an intellectual is defined by depth of thought, quality of thought, and originality of thought relative to the normal expectation. Loading up on intellectual junk food is useless for this purpose, so get rid of it and you will instantly get smarter.

Noticed how I namedropped Murakami there? That’s worth smarty pants points because it’s conceptual tie in that is directly relevant to the point I’m trying to make, and expresses the idea more elegantly than I could on my own. Don’t just namedrop obscure people wildly, as you’ll look more like a jackass than a smarty pants, though the line is blurry at times. Being a fresh-faced smarty pants frequently involves making the people around you feel inadequate, but it shouldn’t when practiced properly!

The purpose of self-enrichment is for self-benefit, and should not be used for putting down others. Frequently, knowledge may be controversial or unwelcome, so begin to be sensitive to that when conversing with others. Life isn’t a contest for who can show off the most factual knowledge– but if it were, a good smarty pants would be in the running for the winner, and that’s your new goal.

Pick an area that will be your expertise. Pick something you will find interesting and can learn about without laboring against your attention capacity. This should be distinct from a hobby. Which topic you address is up to you, but I’d highly suggest approaching whatever topic you choose in a multi-disciplinary manner. If you’re interested in psychology, be sure to devour some sociology. If you’re interested in biology, grab some chemistry and physics. If you’re a philosopher, try literature or history. Your expertise in your chosen field will mature over time, and eventually you should branch out to gain expertise in a new field.

The idea here is that the process of picking an area of expertise is useful to the smarty pants. By evaluating different areas, the smarty pants will get a feel for what they’re interested in, what’s current, and what’s boring. The most intellectually fruitful areas of expertise have a lot of cross-applicability to other areas and concepts, an established corpus of literature, and a lot of superficial everyday-life correlates. Suitable examples of areas of expertise are “the history of science” or “modern political thought”. An unsuitable example of an area of expertise would be “dogs” or “engine design”. Unsuitable areas of expertise aren’t applicable to outside concepts and don’t confer new paradigms of thought.

Start reading books, in-depth articles, and scholarly summaries on topics which you want to develop your expertise in. A smarty pants has a hungry mind and needs a constant supply of brain food, which is synonymous with deep knowledge. Reading books and developing deep knowledge is never finished for the aspiring smarty pants. Plow through book after book; ensure that the most referenced scholarly works or industrial texts are well-understood. Understand who the major thinkers and groups are within the area of expertise, and be able to explain their thoughts and relationships. Quality is the priority over quantity of information, however.

Merely stopping the flow of bad information in and starting a flow of good information isn’t enough to be a real smarty pants, though it’s a good start. In order to really change ourselves into smarty pants, we must change our way of engagement with the world. As referenced before regarding media consumption, a smarty pants must interrogate the world with an active mind rather than a passive mind. What do I mean here?

A passive mind watches the world and receives its thoughts as passed from on high. Passive minds do not chew on incoming information before internalizing it– we recognize this the most pungently when a relative makes regrettable political statements culled directly from Fox News. An active mind is constantly questioning validity, making comparisons to previous concepts, and rejecting faulty logic. An active mind references the current topic with its corpus of knowledge, finding inconsistencies.

Creating an active mind is an extremely large task that I’ll probably break into in another full article, but suffice it to say that the smarty pants must get into the habit of chewing on incoming information and assessing its value before swallowing. Learning how to think/write systematically and disagree intelligently are probably both skills that a smarty pants can make use of.

Speaking of relatives, a smarty pants needs to have good company in order to grow. Ditch your dumb old friends and get some folks who are definitely smarter than you– they exist, no matter what you may think of yourself. You don’t really need to ditch your old friends, but you really do need to get the brain juices flowing by social contact with other smarty pants. There are many groups on the internet which purport to be the home of  smart people, but my personal choice is HackerNews.

It’ll hurt to feel dumb all the time, but remember that feeling dumb means that you are being exposed to difficult new concepts or information. Feeling dumb is the ideal situation f0r an aspiring smarty pants because feeling dumb means that you are feeling pressure that will promote growing to meet the demands of your environment. Every time you feel dumb, catch the feeling, resolve the feeling to an explicit insecurity, then gather and process information until that insecurity is squashed by understanding. Like I said before, this step is unpleasant, but nobody said being a smarty pants was easy.

This concludes my primer on how to be a smarty pants. I’ll be writing more on this topic, though a bit more seriously and more specifically. I’d really like to publish a general “how to think critically” article in the near future, and of course critical thinking is a core smarty pants skill. I have a reading list for the most general and abstract “smarty pants education” that I’ll be publishing relatively soon as well. Until then, try practicing the bold points here.

Be sure to follow me on Twitter @cryoshon and check out my Patreon page!

How to Improve Work-Stuff, Scientifically!

One of my favorite tasks to do when I’m at work is to find ways of optimizing workflows, actions, or processes that I’m regularly doing. If you do something multiple times per day or week, it’s worth doing it as well as possible, right? In my experience, most tasks or workflows are created thoughtfully, but then executed relatively automatically, and, over time, thoughtlessly. Sure, if you have a workflow that’s deeply detail oriented or requires a lot of conscious, brain-on-task time, you’re likely to be mentally active while you execute it, but actually thinking about the efficiency of the process itself may not  be on your mind.

Sometimes I set aside time for process improvements, but usually I fit it into a block of time that I don’t have slated for anything else. Depending on what kind of work you do and what kind of improvements you’re seeking to make, making a change to your process may require a lot of paperwork. If making changes to your workflow or process will require a lot of paperwork, it’s still worth at least investigating whether you can make a change, but the bar for what criterion you use to select your change will probably differ, as it makes more sense to fix a ton of small changes or radically re-haul the process entirely.

When optimizing work, take care to not disrupt old dogmas willy nilly. I propose investigating your workflows scientifically, and determining which optimizations to make scientifically as well. This means that the technique for optimizing workflows I’ll be discussing in this article will be suitable for some kinds of work, but not others. Additionally, my scientific way of investigating beneficial changes may not operate properly for every type of work.

How do you select a process for scientific optimization? The following points are a good guide to seeing whether or not your process can be improved scientifically:

  1. Measurable outcomes and rigorous metrics. In order to think about optimizations scientifically, we need to be able to quantify the pieces we’re talking about. A manufacturing process that produces 15 yellow cubes in 1 hour is an easy candidate for scientific optimization because changes to the process will alter the number, color, or time it takes to produce the cubes. A painting technique that is used to produce impressionistic portraits is not a good choice for optimization, though with some time invested into making qualitative rubrics it may be possible.
  2. Empowerment to experiment. Everyone has bosses, and not everyone’s boss is going to be keen on experimentation with company assets. Having supportive co-workers and bosses is essential to experimenting with process improvements. Bosses may be scared away from the scientific optimization process because it’s resource intensive. Others may be scared due to their own insecurity with scientific pursuits, which tend to be perceived as complicated. Aside from clearance to experiment generally, some processes at work may be open for reinterpretation, whereas others will be sacred and untouchable.
  3. Non-catastrophic failure. Experimenting with the workflow that props up an entire business is sometimes necessary, but should be avoided if it can’t be done safely. The last thing an employee should do is destroy an already-functioning process by attempting to improve it. For some workflows, safe experimentation isn’t possible without the potential for massive fallout if things go wrong. In these cases, making a smaller model to play with typically isn’t possible. I suggest you avoid playing around with systems that will have bad consequences if they fail or have null results.
  4. Controls and Variables. If you’re really going to be conducting a scientific evaluation of your workflows, you need to have the ability to create controls and variables for your investigation. This means that it must be possible to keep the majority of your process the same while changing small pieces individually. Additionally, you need to have data for the way that the process behaves under normal, non-experimental conditions. Most workflows have a paperwork component of some kind, so this is a great place to start looking for control data that you can compare your experimental data with after you’ve run your experiment.

Now you know how to evaluate a process for scientific optimization, so let’s dive right into the meat of how to actually run an experiment once you’ve picked a process to change.

  1. First, if you haven’t already, decide what your variables will be. Remember, you should only be investigating one state of one variable for each trial in the experiment. The variables you pick are up to you, but keep in mind that the items you pick as variables are the items which will end up being improved by beneficial changes to the process that you discover after the experiment is over.
  2. Next, decide your controls. The controls are the most important part of getting usable data from the experiment. I suggest having a negative control (the process as executed before the improvements proposed by the experiment). If you want to get fancy and your process permits it, I’d also add in a null control (a control designed to terminate the process from moving forward) and a positive control (a control designed to test your ability to detect positive results and gather data), but these aren’t strictly necessary.
  3. Once you have decided your controls and variables, it’s time to write up an experimental protocol. How will you be isolating your controls from your experimental group? How will you be altering your variables and setting up your controls? How will you be changing your variables? What will your output look like? How will you be measuring the results of the experiment? How will data be presented in raw form? This is the hardest step and also the most risky step, scientifically. Ensure that your protocol is as close to the normal, pre-experiment way of doing things as possible in order to minimize variability. An experiment is only as strong as its protocol!
  4. Run your protocol and gather data. Each run of the protocol counts as a trial in your experiment. Take care to follow your protocol to the letter, and record data about how the output of the process changes based off of the state of the variables. Don’t worry about analyzing data yet, just try to stick to the protocol and pay attention to your controls and variables. It’s best to minimize variability by running protocols at the same time of day.
  5. Repeat step 4 as many times as needed. Gather data until you feel as though you have enough trials to make a decision. If you want to be super scientific, do some statistics and determine the sample size you need in order to make a good decision, but for most workplace experiments, this level of application isn’t necessary.
  6. Analyze the data gathered in steps 4-5. Which changes to which variables created the most beneficial changes to your originally stated metrics? Were there any consequences to optimization?
  7. Implement changes to your workflow. This should be quite easy, with data in hand. Be sure to argue for your changes using the data that you gathered scientifically, if necessary. If there’s no boss to convince, then enjoy the fruits of your labor immediately.
  8. Show off your good results! Be sure to keep a record of the way that your workflow was run beforehand, just in case. It also helps to maintain records of how your metrics were performing before your scientific optimizations, so that you can show off the positive differences you effected later on. If your results were negative, don’t sweat– most experiments have negative results. More experimentation might be useful, but know when it’s time to throw in the towel. There isn’t necessarily room to improve every single process, especially if it’s already been through the ringer a few times over the years.

Hopefully this guide was helpful to you; I know that I’ve more or less run this regimen on every workflow and process that I’ve touched throughout my professional life. The core concept is systematically tracking changes to variables. As long as you can keep track of what you’re changing, you can make a causative connection between your changes and the outcome.

If you liked this piece, follow me on Twitter @cryoshon and be sure to subscribe to the email list on the right!

Time Management Tips from the HIV Lab

Growing up, I hadn’t ever imagined that I’d be at high risk of HIV infection for years on end as a result of my chosen profession. I thought that HIV was mostly a problem of Africans, or homosexuals in the US– a problem that was steadfastly irrelevant and completely opaque to my white, straight, middle class American self.

When I was desperately scouring for jobs to apply to after graduating from college, my only thought about working in an HIV lab is that it might be a cool opportunity to help people with HIV. I liked the idea of “doing science”, and I liked the idea of “helping people”. I grew to understand that in the course of my work, HIV would be my problem too: the high level goal of my job was to create a vaccine for HIV, and the only way to get there was by slogging through experiments involving HIV+ blood, stool, cell, and tissue samples every day, for years.

When I was interviewing for the job, they told me informally that the rate of infection at this laboratory was %0.3 per year, meaning that if I worked there for three years, I’d have about a %1 chance of contracting HIV due to my own mistakes. I don’t know if that statistic is true or not (I suspect not), but I ended up working there for three years, and definitely had a few close calls due to carelessness– a problem addressed later in this piece. At the time of my application, I wasn’t even a little bit scared. It wouldn’t be until much later that the full meaning of what I was going through would be clear to me, and the caution would take over– far later than it should have, of course.

Formally, my title at the start was “research technician” (how demeaning this term grew to be!) at the Ragon Institute of MGH, MIT, and Harvard, an academic research laboratory group devoted to the formulation of a vaccine or cure for HIV, an immune system disease that has proven to be increasingly problematic in the developing world.

At this early point, I hadn’t yet understood that HIV was a problem outside of poor and minority communities. Luckily, I joined the Ragon book club, and read a biography called A Song in the Night written by one of our research subjects regarding his fight against HIV. Reading his account of HIV divested me of my delusions, and made me think more about the white, straight, middle class HIV epidemic that was largely hushed up during the early stages of the disease’s spread.

My job at the Ragon Institute (or Ragon for short) was my first “real” job after college, and I experienced a huge amount of personal and professional growth during my time there. In the course of my time at the Ragon, I went from being a lowly “pair of hands” quasi-biorobot to being one of the most experienced technologists in the Institute,  responsible for leading, training, and advising my peers.

The biological sciences are extremely demanding in terms of attention to detail, and immunology is no exception. Each experiment must be designed properly, and executed with caution and precision. In order for experiments to have statistical relevance, they must be repeated many times with slightly different variables, leading to a high volume of work.

The work must be performed in standardized ways, making use of components which have been tested and standardized themselves. These factors quickly create workflows that are extremely time consuming, dangerous, and psychologically demanding, generating stress. A tiny mistake could ruin a week long experiment, wasting time and money. A larger mistake could give you HIV.

This piece will chronicle the distilled professional wisdom from my time at the Ragon Institute, with a special emphasis on time management.

Many of my nuggets of wisdom have been culled from times when I made mistakes, or witnessed others making mistakes, frequently as a result of rushing through an experiment in a stressed out fashion due to fear of reproach and political fallout.I also frequently consulted Extreme Productivity, which is a decent resource for jump-starting your own thinking about improving your work experience.

After a year of working at the Ragon, I realized that I needed a solution to the problem of making easily avoidable mistakes in order to save my sanity. The mistake that prompted this thought occurred when during an experiment, I performed an action that was akin to adding dish soap directly into a fresh cup of coffee that you’re planning to drink. It was a mixup of order, and relatively simple to avoid. I figured that the solution to avoid making the harder to avoid mistakes would become evident if I managed to find a technique for the small ones. I wasn’t wrong– any problem that’s large is a problem that’s waiting to be split up into particle-sized steps which are easy to solve.

First off, I figured I’d decrease the speed at which I worked. This seemed like a pretty basic common sense way of reducing mistakes. Later, I’d reform this idea to fit it into my concepts of stress reduction and time management, but at this early phase, I didn’t quite execute it properly. I pledged to slow down, especially when doing “simple” tasks. I didn’t think about breaking down large tasks into smaller ones, or planning more effectively, or making accommodations for my reduced rate of perfectionist work.

As a result, when I slowed down, I’d quickly have a backlog of work, and trouble making my appointments and reservations to use certain instruments or people’s time. Sure, the work that I produced didn’t have quite as many mistakes– until I began to get stressed about the growing pile of work yet to be done as a result of my slowness. Then, the growing stress would take its toll, causing mistakes on the more complicated manipulations of my experiments.

The missed and late appointments and reservations were also a stressor, causing tension with the other people in line to use the various research apparatuses. Just slowing down without taking anything else into consideration definitely wouldn’t work. With some trial and error, I made a system for improving my work quality.

My system has three main parts, and one main variable. The three parts are time management, stress management, and professional relationships. I’ll be focusing on time management in this post. The variable is perfectionism. I’ll describe the other parts of the system separately in a different piece. Your time management strategy must be consciously calibrated for the job at hand in light of perfectionism. The level of perfectionism that you choose to apply is going to have transformative impacts on the details of your time management, your levels of stress, and your professional interactions.

I’ll explain in more detail how perfectionism fits into each piece as we go, but the main theme is that perfectionism is a sliding scale which has both good and bad consequences. In the HIV lab, I occupied every shade of the perfectionism gradient at one time or another, often unwittingly. As a novice, I had no control over my own level of perfectionism or lack thereof, meaning that simple but inconsequential tasks (slapping labels onto vials) were performed slowly and perfectly, whereas deeply difficult and complex tasks (calibrating the cytometer’s laser voltages to prevent spectral overlap of excited-state flourochromes) were breezed through without care. When I reached mastery, I understood how to regulate my own level of perfectionism to best complete the tasks at hand. I hope to share this ability with you.

The first step in revamping my time management ability was to estimate and then measure the amount of time that it took me to perform various common tasks. I measured how long it took me to prepare my samples for the analyzer machine, and then how long it took me to analyze them, including the physical walking time to transition between the two places I’d need to go. I measured how long it took me to manipulate my samples if I did preparatory work during my otherwise unproductive incubation times. I measured how long it took me to add entries to our sample database. I measured how long it took me to jot neatly into my lab notebook, and, for fun, measured how long it took me to merely scribble unintelligibly into my lab notebook. Attention to detail takes time.

I wrote it all down, and had a nice collection of most of the things that I did and about how long they took me, along with a few variations of those common things and the extra time the variants would take. This is a critical step to time management, and I highly encourage you to do the same: think of things you do frequently, time yourself (even if you think you know how long it takes, get an objective measurement!) and write it down. Once you have several pieces of data for each task you commonly do, you are closer to being ready to making a realistic work schedule for a given day.

Before we get to actually making the schedule, there’s one other thing that I learned which is critical: breaking down tasks into particles and tracking completion of each particle like a tyrant. It’s an old piece of advice, but it actually works. Don’t write a plan and have an item that says “do the laundry” with an estimate of two hours. This is asking for stress, because in order to do the task “laundry” you have formed the idea in your mind that it will take 120 minutes of continuous work, which is not true. Doing the laundry isn’t all one step, either. It’s a common work flow with a few different steps that fits into your larger plans for the day, and comes with transition times between steps which can’t be neglected.

In order to put the concept of “doing the laundry” into your schedule, it should really look more like:

Laundry (estimated time 2H total):

  1. Gather the dirty clothes (2 minutes)
  2. Separate the white clothes from the colored clothes (3 minutes)
  3. Put the dirty clothes in the hamper (1 minute)
  4. Grab the detergent (30 seconds)
  5. Bring the detergent and the hamper downstairs (1 minute)
  6. Put the detergent into the washer (30 seconds)
  7. Put the clothes into the washer (1 minute)
  8. Start the washer (15 seconds)
  9. Wash cycle (35 minutes, could do something else in the meantime)
  10. Remove the clothes from the washer (2 minutes)
  11. Transfer clothes to dryer (2 minutes)
  12. Start dry cycle (15 seconds)
  13. Dry cycle (50 minutes, could do something else in the meantime)
  14. Remove clothes from dryer (2 minutes)
  15. Fold clothes (10 minutes)
  16. Bring the folded clothes and detergent back upstairs (1 minute)
  17. Put away the detergent and the folded clothes (10 minutes)

None of these steps are intimidating whatsoever, and you can adjust the times to be more accurate as you go. You may also notice that there are a few opportunities here to reduce the amount of “hands on” time. If you were to gather the dirty clothes, separate them, and put them downstairs the day before you had to do the laundry, for instance, that’d reduce the amount of time you’d have to be working on the day of. In this case, the prep work wouldn’t make a huge difference in reducing the total amount of time spent on the task, but it certainly would give you more flexibility to fit doing the laundry into a given slot of time, because it would take less time on that day.

Doing the prep work before it was actually needed was a lesson which also greatly improved my ability to multitask. Once you have made lists with particles of things to do for a given task, you can very easily fit your overall schedule together such that while you are doing hands on things for one task, a different task is in its incubation time. This also works for situations in which you hand off your work to someone else, who will later return it back to you. It’s nice to rest sometimes, but this is time that can be used to be productive. If you hand off your work, you can usually make headway on something else in the meantime. If you’ve done your prep work or do your prep work during these times, you’ll find that you can knock down a lot of tasks by just fitting task particles into any open space.

Realizing that turning my tasks into particles allowed me to accomplish more by cutting down my dead time was a huge improvement for my work at the lab. Of course, multi-tasking has consequences: a task with maximum perfectionism applied will be performed alone, so as to allow a full devotion of attention.

Doing something while something else is out of your hands is a basic productivity strategy, and it also implies another good trick for scheduling your particle-sized tasks: leave yourself a margin of error. If you can plan your day to the minute accurately and have no spare time whatsoever, you’re overbooked. In the lab, I always left myself extra loose time in my schedules in order to account for things which might pop up: a coworker asking for help, running out of a reagent and needing to borrow it, a fire drill, coffee with a friend passing by, etc. You need this loose time as an insurance policy against fate, and also for your own sanity. Having extra time to play with often means that you have more time to take more care and more perfection in the tasks that you are doing. Not having extra time means that in the event of anything unexpected, you will be behind schedule, and your tasks can’t be attended to as much as they really should be.

The last major consideration is the amount of perfectionism you are going to invest in each task in your day. I suggest an easy rating scale of 1 to 5, with 5 being tasks that require an extreme amount of care and perfectionism and 1 being tasks which can be breezed through without much fear of a mistake causing a major derailing. Each particle in your list of tasks on your schedule can be rated this way. This way, you can allow yourself to relax a little bit in between focus intensive tasks while also understanding when you are going to need to really put in scrutiny.

Judgments of what amount of perfectionism should be formed based on the ease of the task, the ease of correcting mistakes, and your familiarity with the task. If it’s quick, easy, hard to mess up, and simple to fix, the task is a 1. If it’s extremely involved with irreversible consequences in the event of a mishap, it’s a 5. This system can help to relieve stress or at least channel stress at the correct moments as well. A quick self-reminder that “this task is a 1” or “this task is a 5” helps keep things in context. In lab research, far more things are closer to 5 than to 1.

In summary, time management is absolutely critical, and easily separates effective and productive employees from those who are drowned, stressed, and overwhelmed. It is a common story that giving an extra task to the busiest person results in it getting done the fastest. I think that this story is a result of the superior time management and productivity dispositions that the highest producing people have to have.

To a certain extent, a person that is an effective time manager is a lot like a wood furnace for tasks. There is a finite capacity for how much a wood furnace can burn at any given time, but its response to having wood put into the fire is to burn hotter and more efficiently.

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How To Write Systematically in 11.5 bites

After a few years of working in biomedical research and a philosophy degree from college, I know a few things about writing and thinking systematically. Unfortunately, I see a lot of people stumbling in their writing when they try to create complex abstract or technical materials– writing is tough, and accurate, succinct, detailed, and logical writing is even harder.

To me, systematic writing is a method of writing which seeks to transmute the complex relationships between raw or parsed data into a coherent, readable narrative that can be effectively understood and analyzed by someone who is generally knowledgeable on the topic, but who didn’t gather or prepare the data. Systematic writing is part of a greater family of writing that includes scientific writing, technical writing, and financial writing, along with other types I probably haven’t even thought of.

While this definition may seem overly abstract, I’d like to point out that most of our received and sent communications are not systematic; a news anchor is not relaying systematically prepared information to the public, even though the reporters have gone through the trouble of parsing raw data (events that happened) into a narrative (what the anchor says). The quantity of technical detail and data referencing in a news report is slim, as news reports are designed for a very wide audience who have little previous context for the event that happened (the data). An email we send to a colleague referencing data or analysis is not necessarily systematic writing, as it’s entirely possible for a certain context to be inferred between two people; systematic writing provides its own context and content explicitly to the audience.

Systematic writing is typically intended for a small, already-savvy audience, and should only offer the minimal viable context. A reader with general knowledge on the topic of the piece should be able to acquaint himself with a systematically written piece in short order, but a layman should not, because establishing the amount of context required for a layman would involve a lot of background information which falls outside of the scope of a particular instance of systematic writing. We don’t want our systematic writing to sprawl, because systematic writing is intensely purposeful and detail-heavy writing, and lots of background information and tangents dilute the factual details we’re trying to communicate.

So, the title promises 11.5 bites describing the process of writing systematically, and without further ado here’s a primer on how to write and think systematically:

  1. Define your goal. What kind of narrative do you want to make, and what data are you planning on using? Who is going to read the report, and how much context will be required?
  2. Put on your white thinking hat.  To use the terminology of the fantastic thought guide Six Thinking Hats, the white thinking hat is purely unbiased and factual thinking used for establishing a common ground among readers. If you’re going to be writing a systematic document which refers to data, you need to make sure that you don’t take any liberties with the data without explicitly qualifying them as speculation or partially supported. No spin!
  3. Assemble your data. You can’t write systematically without having data. Ensure that your data is collated/parsed/charted in a non-deceptive and easy to understand way– the only person you’re trying to inform at this step is yourself, so it behooves you to be honest about the quality of your data and what knowledge we can actually extract in analysis. If there are computations or manipulations required of your data, now is the time to do them.
  4. Determine the limits of what your data can tell you. Soon, we’ll analyze our data, but first, we need to vaccinate ourselves against narrative mistakes. Though it seems simple, it’s easy to slip up and attribute facts to your data that aren’t actually there. Explicitly state the variables which your data depicts (sales, months). Remember that going forward, all of your statements should be in terms of the variables which you outline here. If you’re not talking about information within the purview the data that your variables describe, you’re not being systematic.
  5. Extract verbal information from your data.   Write down simple statements to these effects,  such as, “the data for November showed 42 sales.” If you computed averages or other values in your data assembly step, now is the time to introduce it as a simple phrase. If you expect that handling the data in this way will be confusing, document your process simply and clearly so that your audience will understand. Do not introduce any explanation at this point, merely state what the data say, and, if necessary, state how the data were processed. Remember not to speculate, the point of this step is to establish purely factual statements.
  6. Analyze your data at a basic level. Now that you have a series of simple statements depicting your data in an unbiased way, comparisons between data statements can begin. Are the sales from November higher than the sales from October? Write that comparison down if it’s relevant to your originally stated goal, and make sure to directly reference the values in your new synthesis statements. The point of this step is to explicitly state simple relationships of the data, independent of any narrative.
  7. Analyze your data deeply. Stay focused on your original goal during this step. What questions can your impartial data statements answer explicitly? Implicitly? What trends in your data are noteworthy? What points of data are outliers? Can you explain the outliers? In this step, writing more complex statements is necessary. “The sales data from November (42 sales) are higher than October (30 sales), following the upward trend of the fall season. These data tell us that the fall season is our strongest selling period, despite the high sales in December.” Don’t try to speculate or hypothesize about “why” yet, just tease out the more complex relationships in your data, and write them down in a clear way. As always, reference your data directly in order to build context for your audience and keep them on the same page. Don’t worry about over-analyzing at this point, we’ll prune our findings later.
  8.  Ask Why. Why did we see the data that we saw in our analysis? What are the general principles governing our data? Address each piece of relevant data with this question, and ensure to answer it briefly. The outliers that were previously identified need special attention at this point. Keep explanations of your data concise and factual, though remember that your explanations are not actually within your data set, so you should draw in outside proof to support your explanations if necessary. It’s okay to hypothesize if you don’t know exactly why certain data turned out the way that they did, but be sure to explicitly label speculation.
  9. Build a narrative using your data, analyses, and explanation. Consider your starting goal, and how to marshal the data, analyses, and explanations in order to accomplish that goal. Your narrative should proceed first with the data, then with a simple factual explanation of the data, then with a more complex analysis of the data, and finish off with an explanation of the data if it’s required. The narrative step of systematic writing is where you put all of the pieces together and put it into one attractive package for your audience. Don’t neglect graceful segways between different portions of the data set. The final product of this step can be considered a first draft of your systematic writing effort, and may take the form of a PowerPoint presentation, meeting agenda, technical report, or formal paper.
  10. Anticipate questions and comments from your audience. Look for areas in which your explanation, analysis, or data prompt a response, and plan accordingly. Questions regarding your narrative are typically the easiest to address by clarifying what you’ve already written explaining why your data appears the way it does. Questions regarding your analysis can get a bit technical depending on the audience, and so you should be prepared to refer back to the source data in your responses. Questions regarding the data itself  or the parsing of the data are the most difficult; typically, the outliers will be under the most scrutiny, and their data quality may be called into question. I find that it helps to get out in front of questions regarding outliers, addressing them to your audience before taking questions.
  11. Prune non-critical information. This is the step where most of the data-statements and analysis statements meet their demise. Which analyses, explanations, and narrative elements aren’t strictly serving your original goal? Remove extraneous information to create a hardened product. Ensure that the relevant context and core data analysis remains, and don’t build a misleading narrative by omitting contradictory relevant data.

The final half-step is, of course, crossing the t’s and dotting the i’s for your final draft– and make sure it’s perfect! A missed detail on something not mission-critical will still distract your audience from your data and analysis.

I hope that my readers have a better idea of how to write and perhaps think systematically after reading this piece. I think that many non-technical people struggle with systematic writing because of how data-centric it is; communicating in the style of referencing data and withholding speculation can be quite difficult for people accustomed to relating written concepts intuitively and emotionally.

If you have any questions, leave em’ in the comments and I’ll respond. I know that the 21st century will have the highest demand yet for systematic thinkers and writers, so I’m also considering forming a consultancy in order to help organizations with training their employees and executives to think and communicate in systematic ways, so expect more on topics like this in the future.

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