Drawing Logical Conclusions from Experimental Data

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  • 0:01 The Final Step
  • 0:57 Interpreting Data
  • 4:12 A Subjective Process
  • 5:41 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.

Experimental results are what scientists like to share with each other, but it's important to understand what those data mean. We do this in the final step of the experimental process, when we draw meaningful conclusions from the results we obtained.

The Final Step

Completing a research project is a very satisfying feeling. And you should feel good! You put a lot of work into formulating your hypothesis, designing your experiment, and analyzing your data. But quite possibly the most important step still remains: explaining what it all means. As they stand right now, your results are like a bunch of characters in a book without a story.The components are there, but they need to be given some context and purpose.

This final step is so critical because it allows you as the researcher to bring it all together. This is the time to explain not only what your data mean for your experiment but also what they mean for future scientific studies and the benefits they provide for the scientific field. The last step also allows you to describe what you learned from the experiment and any possible issues in either the experimental design or analysis.

Interpreting Data

In order to do this, you as a scientist must look very closely at your data to determine what they mean and any implications they may have. Believe it or not, you go through this same process all the time in your daily life! We call it drawing logical conclusions, which is just evaluating information and making appropriate judgments.

Think about how you get dressed in the morning. What you wear depends on a number of different factors. What will the weather be like that day? Will it be hot or cold? Is it going to rain? Will you be going into the office or doing something else? Let's say, for example, that it is winter and it is a Saturday. During the winter, it is cold, and on Saturdays you take your dog to the park. Based on this information (the data), you can say with a good amount of certainty that you will need to wear cold-weather clothing, such as warm socks, long pants, and a coat. You will also need to plan for being outside, so you decide to wear a hat, gloves, and maybe even a scarf.

What about when you make lunch? Here, you need to consider what food you have in your fridge, how much time you have to cook, what you already ate for breakfast (because who wants to eat the same meal twice in a row?), as well as if you have dietary restrictions (like being a vegetarian). Based on all of this information, you can come to a logical conclusion about what to have for lunch that day.

These are, of course, very simple examples. But, can you see how pieces of information take on new meaning when you put them into context and draw some conclusions from them? Let's look at a more scientific example to see how you might follow the same thought process to interpret your experimental data.

Let's say you ran an experiment to better understand plant growth. You had three groups of plants: one that received fertilizer and two that did not. One of the non-fertilized groups received three more hours of sunlight exposure than the other two groups of plants, and the third group of plants received nothing. In the end, the group that received the fertilizer had the greatest amount of growth, the group that received neither fertilizer nor extra sunlight had the least amount of growth, and the group that received no fertilizer, but did receive extra sunlight was somewhere in between.

What conclusions might you draw from these results? You might say that both fertilizer and sunlight led to an increase in plant growth since both of these groups grew more than the group that received nothing. You might also conclude that fertilizer had a greater impact on growth than sunlight, since the fertilizer group grew more than the extra sunlight group.

Based on the preliminary data, these conclusions make sense. But now your results are so much more meaningful because, instead of just presenting some arbitrary plant growth values, you have explained what the numbers mean in a given context. Additionally, you might also conclude that further studies are still needed because now you have more questions. Such future studies might test whether different types of fertilizers affect plant growth differently, or if the amount of fertilizer plays a role in plant growth. You might also test sunlight alone (leaving fertilizer out of the experiment) to determine the optimal amount of extra sunlight for maximum plant growth.

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