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What's in an error bar anyways?
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Nick Fahrenkopf
Albany, New York

In 1955 while addressing the National Academy of Sciences Richard Feynman stated "Scientific knowledge is a body of statements of varying degrees of certainty." As usual, Feynman's statement was spot on, and holds true decades later. In his famous "Plenty of Room at the Bottom" lecture Feynman talked about what we now call nanotechnology, and all the different applications. Here I am, half a century later, working "at the bottom" and living in a world of uncertainty. I hope to share some of the exciting discoveries at the nanoscale and explain how they apply to my passion of biotechnology- as well as the everyday world. Learn more about Nicholas Fahrenkopf

My posts are presented as opinion and commentary and do not represent the views of LabSpaces Productions, LLC, my employer, or my educational institution.

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Comment by Nick Fahrenkopf in What's in an error bar anyways?

lkasdjfsaid: The difference is not in the fields of study, but rather in the two different types of work . . .Read More
Nov 27, 2012, 9:34am
Comment by Nick Fahrenkopf in What's in an error bar anyways?

Brian Krueger, PhDsaid: Since you're working on semiconductor sequencing, what do you think of Oxford Na. . .Read More
Nov 27, 2012, 9:28am

Good one . . .Read More
Oct 15, 2012, 12:42am
Comment by lkasdjf in What's in an error bar anyways?

The difference is not in the fields of study, but rather in the two different types of work being done.  In the example, the EE is making an new device,  -- i.e. developing a new type of technolo. . .Read More
Sep 07, 2012, 11:38am
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Thanks to Flickr users kevindooley and DESQie for their art I integrated into the blog's header image.
Saturday, November 19, 2011

I’ve unfortunately had to sit through some very rough presentations lately, so in everyone’s best interests, here is my second volume of things to think about when giving a presentation (see: Ten Tips to Give Great Thesis Defense). In this case we won’t be looking so much at the presentation, but instead the experiment and how small oversights can blow up in your face during a presentation. I could go on forever about these kinds of things, so for now I’ll focus on four things.

 

Error and propagating error

It is highly unlikely that your data is absent error. Even a simple experiment measuring the length of an object will have error. If you measure the same object a few times there will be some difference in the value obtained from measurement to measurement. This is one source of error and should always be reflected in your presentations. That is, “how repeatable is your data?” Further, if you measure the length of an object and use that measurement to calculate another value- say volume- you need to propagate that error and report that in your presentations. I won’t go in to HOW you propagate error, but you need to! Say you measure a dimension of a block to be 1 +/- .25 meters. That’s fine, even with that error you can tell the difference between 1, 1.5, and 2 meters. But say you do that measurement (with that kind of error) and then try to calculate area. A 1m x 1m block is 1 meter squared… +/- .35! That is, the error gets worse as you start calculating from measurements. That that into account, and be upfront about it when you present.

 

Significant figures

There are of course other errors you need to worry about. Say you’re taking contact angle measurements, and the gradations are for each degree. You take three measurements and come up with an average. You can’t show the measurement to be 47.89 degrees- those aren’t significant figures! That’s 47.9 degrees. Further, the error you report should have the same amount of significant figures. Also, you have to remember to take into account the error in making the measurements, and the standard deviation in those multiple measurements. Again, I won’t go into detail on how to take all these things into account, but you have to be thinking about these things! Lastly, use some common sense. You have no idea how many times I’ve cringed when someone reports the thickness of a thin film to be 7.65 nm +/- 0.04. Four tenths of an Angstrom?! Really?! You’re THAT good?

 

Precision vs Accuracy

Besides the precision of (and error associated with) measurements, you need to consider the accuracy. Simply put, what if your meter stick was bought at a dollar store has been beat up around the lab for a few years, broken, taped back together and now you’re taking your big measurement? You might be able to get the same measurement time after time but there’s nothing saying it’s the RIGHT measurement. To deal with this kind of error you often need to qualify your measurement technique with another technique that is calibrated, more direct, and usually more time consuming (it doesn’t NEED to be, but it usually is or else you’d already be doing it!). For example, I use a machine to deposit thin metal films. In it is a sensor that tells me how much metal I put down with a precision of Angstroms. But when I check the thickness in a couple of different analytical tools I find out I put down 15-50% more metal than the deposition machine said I did (depending on the metal)! We found that if you adjust based on the percentage and we can get it spot on. That is, the sensor is very precise, just not accurate. If a measurement is important in your presentation, double check it using a different technique.

 

Is there a better way?

That brings me to my last tip- maybe there is a better, more accurate, easier way to take that measurement. If you’re not already, you should be presenting your research REGULARLLY to people not in your lab*. You don’t want to go to a conference, present the results you’ve been slaving over, and then get a question “Why didn’t you just do X?” (Where ‘X’ is easier, faster, better, and cheaper.) Those are really tough questions to answer, and you’ll end up with “Hmmm, well I didn’t think of that, thanks for the suggestion.” That’s not a terrible answer, but it feels awkward to stand in front of a group of people who know you wasted a lot of time because you didn’t know about ‘X’. For example,  I didn’t have a labeling kit handy to assess the amount of immobilization of proteins to a surface. But, our college had an XPS system that could measure changes in atomic composition of surfaces. It isn’t often used for biological experiments but what’s the difference between measuring atomic composition of metal or semiconductor surfaces and measuring the composition of a thin film of protein? Not much, it worked, and was really easy. If you’re not interacting with people from different disciplines you won’t learn their tips and tricks which can save you time, resources, and awkwardness.

 

To summarize

I’m sorry this article won’t make your presentation magically better, and it won’t sugar coat bad experiments or data. Instead, I hope as you are formulating and executing your experiments you remember the propagation of error, precision, accuracy, significant figures, and to think outside your field sometimes. If you do that, you’ll be in a better place when it comes times to prepare your presentation and actual present. The best presentation skills, and best laid out slides can’t save you from bad experiments.

 

* I have four meetings a week: Monday lab meeting to discuss day to day operations, Tuesday department meetings where two students present to the whole department each week (3-4 month rotation), Wednesday sub-group meetings for 4-5 students to discuss their research informally, and Saturday (::gasp!::) college-wide meetings for 1-2 students to present their work to the whole college (annually). All this gives a lot of opportunity for new eyes to suggest different techniques. The key again is people outside your field!

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yannisguerra
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Nice. This is a very useful post. It gives both the learning points and some solutions at the same time. Great work.

 

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