Identifying Potential Reasons for Inconsistent Experiment Results

Identifying Potential Reasons for Inconsistent Experiment Results
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  • 0:01 Inconsistent Results
  • 0:37 Uncontrolled Conditions
  • 2:07 Random Experimental Error
  • 4:22 Lesson Summary
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Lesson Transcript
Instructor: Sarah Friedl

Sarah has two Master's, one in Zoology and one in GIS, a Bachelor's in Biology, and has taught college level Physical Science and Biology.

Repeating an experiment helps ensure that you designed and implemented it correctly. But what if you don't get the same results each time? In this lesson, we'll explore potential reasons for this and how you might avoid it in the future.

Inconsistent Results

A good scientist knows that one of the best ways to ensure that your experiment was performed correctly is to run it several times. If all goes well, you should get the same results each time. But sometimes you get different results, which can be really frustrating! You thought you were diligent, careful, and thorough, but there must be something wrong somewhere because you got something different every time.

There are two main reasons your results may not be consistent: error and uncontrolled conditions. The good news is that once you learn about these and how to plan for them, most of the time, this type of frustration can be avoided, and your experimental results will be more similar than not.

Uncontrolled Conditions

Something that you should always be aware of when designing and running experiments is controlling for any potentially confounding factors. You may remember that a variable is any parameter in an experiment that can change. This includes all sorts of things, like weight, temperature, and growth rate.

A control variable is one specific type of variable that is controlled during and between experiments. We call it the control variable because we are controlling its influences on other variables in the experiment.

For example, if we're interested in knowing how plant growth is affected by fertilizer, then we would need to control for other factors that affect plant growth, like water, air temperature, and sunlight. To control for these, we would simply make them the same for each plant. So if we decide that the plants will receive 6 hours of sunlight and two 1-ounce waterings each day and that they will be housed in a room that is 75°F, then these factors have to be the same for ALL plants in the experiment.

In this case, the conditions of the experiment are controlled - we have made sure that nothing other than the amount of fertilizer is different for the plants. That way, we can be sure that fertilizer is the only thing affecting plant growth.

You can probably see where I'm going with this. Uncontrolled conditions are likely to affect your experiment in ways you don't want. If you want to know about the effects of plant fertilizer, but you don't control how much sunlight the plants get each day, you have no idea if the plant growth was from the fertilizer or the sunlight. You are also likely to get a different result each time you run this experiment because the amount of sunlight may be different each time.

Random Experimental Error

Controlling your experimental conditions is a pretty easy way to reduce the chance of having inconsistent results. One thing that's not so straightforward is experimental error, which is the difference between a measurement and its accepted value. We're not talking about human error here, like spilling a jar of chemicals or forgetting to turn off the lights in the greenhouse. What we mean is anything that causes our data to be imperfect.

To understand what this means, you'll need to know a little bit more about data. Don't take this the wrong way, but technically, all of your data are false. This isn't a criticism; it's just that data are only representations of our observations of the natural world. We can never collect 'exact' values and measurements, though we can get pretty close. And because no data are perfect, they inherently have some sort of error associated with them, which we call random error, or error that occurs randomly in space and time.

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