Science Q: When Lab Results <> Reality

Here’s a stupid question which isn’t so stupid, afterall! Hear me out on this, please. How do we know for sure that results observed in a lab truly model results in a real-life situation? This thought has crossed my mind, but I used to laugh it off as silly…until now!

I was reading in a biology text(1) about the study of photosynthesis in the lab. It seems, when chlorophyll was isolated in the lab, and exposed to sunlight, it actually emitted light(2) …instead of a free electron from the chlorophyll molecule necessary to start the process of photosynthesis…as predicted by all the biochemistry theories at that time. Not sure how the real story of what is happening in plant cells (vs. the test tube) was finally determined.

So, how do you go about studying something that behaves differently in the lab, and how often is this taken into account by medical researchers, etc…? Maybe some SDoper might know of another case history like this to share with us?

  • Jinx


(1) The text is: Biological Science by Dr. Keeton (of Cornell), 3rd Edition, 1980, WW Norton Publishers, New York.

(2) It should be noted that the light entering had different characteristics than the light emitted, so you don’t think it is the same light simply being transmitted on through the chlorophyll

You have been misled. Chlorophyll fluorescence (which is what you’re describing) is a loss process, not a mechanism; the pathway of photosynthesis is indeed “electron transport … leading to … the reduction of NADP+ to NADPH, membrane proton transport and eventually to ATP synthesis.” However, “approximately 3%-9% of the light energy absorbed by chlorophyll pigments is re-emitted from the first excited state as fluorescence.”

To answer the more general general question, when experiments disagree with expectations from theory, you look closely at the experimental setup. If the experimental results are valid, write a paper on it. Perhaps the theory needs to be modified. This is how science works. Experiments that contradict theory are a good thing, because they expose something we don’t understand.

It is an important question, and all I can say is that scientists try hard to take that into account.

Since the laboratory is, after all, part of the real world, the lab results are reality. But what you’re asking about is a little different. Often, one will study some complex natural process by modeling it with a simpler, but similar, process in the laboratory. With photosynthesis, for instance, the complex process involves an entire plant and all of its parts, while the model might just consist of chlorophyll in a jar. And sometimes, it’s true that the model isn’t close enough to the system of interest. That’s why you do multiple experiments, using different sorts of setup, etc. If your model is inaccurate, then sooner or later an experiment is going to show that inaccuracy. If you’ve done many experiments and you haven’t seen any such problems, then you can be confident that your model is accurate.

Just to be clear, you are referring to “inaccuracy” based on comparing results to what happens out of the lab, because otherwise I think there’s a tail wagging the dog somewhere otherwise… ?

If you’re talking about Biology, especially medical research, then…

IN VIVO - inside an organism

IN VITRO - separated out (sitting in chem glassware)

There are all sorts of effects which crop up when things inside a living system are pulled out and examined separately, or when things which were developed in the bio lab are tested in an actual organism.

I have this thing that has been bugging me for many many years.

In high-school chemistry, we did this experiment where we took ice and brought it to boiling point. Being a smart young kid I knew what I expected. Once started, the temperature would rise a certian number of degrees every 10 seconds.

And it did. But when the temp approached boiling point, it seemed to slow down. But (being a smart young young guy), I falsified the data, knowing that I had read the therommeter wrong).

It ended up that the experiment wanted to demonstrate latent heat - a well known quantity.

My confidence of the outcome had greatly effected the experiment. Since then, I have wondered if I was the only one…

It is a bit different in school where you are not truly doing experiments and you know you will be marked wrong if you don’t get the expected answer. Unfortunately it happens in real labs sometimes too. That is why people repeat experiments. One of the little known facts is that Mendel misreported the results of his pea experiments since modern statistical analyses show the correct expectation but too little deviation from the average. The thought seems to be that he found his results and then prettified the data to make them more impressive. A lot of good that did him, but statistics was a new science.

Actually there exists a branch of philosophy of science that defends the viewpoint that lab experiments are constructs of reality that do not necessarily represent lived reality. Experimental results, they say, are artefacts of these experimental set-ups (such as all those fancy machinery which you will not see in normal life). Latour (a French philosopher of science) is the main proponent of this. Is that the kind of view that the OP meant?

For the record: I do not side with Latour and his buddies. Although it is good that they make you look at what actually goes on in experimentation (including the effect of expectations, about which the previous posters mentioned examples), in the end it is too quick to say that everything is a artefact in the strong sense of not having any bearing outside the lab.

Science is a process of discovery and study, if you truely knew what the answer was, there would be no reason for the experiment. As someone who works with researchers in a rapidly evolving field of study, maybe I can give you some insight into how we deal with experimetnal data. When you plan an experiment, normally you are testing a theory, and you have an idea of what you think the out come of the experiment will be.
Then you do the experiment. If the outcome is as you predicted, then the results of that particular test support your theory. And you go on to do more tests, and get more evidence to support your theory, if everything works out, you publish a paper on your theory, and people read it.
But, the world is a tricky and complex place, and experimental results often do not agree with the outcome you predicted. Good scientists do not fudge data, because if they did, it would be discovered when they attempted to publish their data, and their career would be seriously effected. If data and theory don’t agree, your next task is to figure out why they don’t agree. There are a huge number of possible reasons, but I’ll list a few basic examples: (Note: I work mostly in biochemistry and chemistry, so my examples may be a bit biased in that direction)

  1. you screwed up (e.g set up a machine wrong, did your calculations wrong somewhere along the line, let something get to warm, added the same reagent twice etc, the gods were against you, the moon was in the seventh house etc.)
    Solution: repeat the experiment
  2. something is wrong with your materials (e.g. chemicals have degraded, pipettemen are not calibrated properly, sensors are bad, computer programs are faulty, someone in the lab put water in the ethanol bottle etc.)
    Solution: Get new materials, try the experiment at someone else’s bench, call someone like me and complain about chemicals.
  3. something is wrong with your experimental design. (perhaps the experiment you’ve set up isn’t really measuring the effect you think it is, or perhaps the conditions you are using are not close enough to those found in nature.)
    Solution: try to rethink your design, can you test the same theory through a different method? If so, try the second method, and see if the results agree with your original theory, or with your intial test results. If you’re trying to model a natural system, try researching literature, see if you can find a better model
  4. something is wrong with your theory. (Usually, we formulate theories based on the findings of previous researchers. If your results don’t agree with the results predicted by earlier work it could be because your system is different, and therefore the theory is not valid in your system, it could be because the earlier research was flawed, or it could be that you have discovered something new that changes our understanding of previous research)
    Solution: do endless literature searchs to see if there is information out there that may alter your theory. talk to other researchers, look for insight, again, call people like me for suggestions, do more tests.

Basically, experimentation is like solving a puzzle, the fact that the data generated by your experiment does not agree with your theory, is neither good, nor bad. It is an oppurtunity to learn, and maybe fit together a new piece of the big puzzle

Albert Einstein said it best: " If we knew what we were doing, it wouldn’t be research."