Identifying & Explaining Patterns in Scientific Data

Instructor: Laura Foist

Laura has a Masters of Science in Food Science and Human Nutrition and has taught college Science.

We need to know how to analyze the data that we get from scientific data. By analyzing the trends and patterns in data we can understand what the experiment results are.

Analyzing the Data

You have finally completed your experiment and have all of this data. Now what does it mean? How are you supposed to understand how it all answers your question? Did your hypothesis remain unchallenged?

There are several different things you can do to determine what the data is telling you. You'll need to be able to look for any patterns that may exist, and organize the data in certain ways. This will depend on the type of data collected and what you are trying to determine.

Comparing Two Groups

Let's say that you learned that the median age in the United States was 38 years old. You think, ''I think that the people who live on my street are a lot younger than the national average.''

So you made an observation, then you created a question where you asked ''Is the median age of people living on my street the same as the United States National age?''

You go out and find the ages of everyone living on your street and got about 20 different results.

First you need to determine the median age of this data. How do you do this? One way would be by creating a stem and leaf plot of the data. A stem and leaf plot organizes the numbers from least to greatest by putting the first digits as the 'stem' and the last digit as the 'leaf'. So first we organize the data from least to greatest: 1, 2, 5, 8, 11, 15, 15, 16, 25, 35, 35, 36, 41, 43, 55, 65, 72, 76, and 99. Now let's put it into a stem and leaf plot:

First let's find the median. There's a total of 19 data points, so the half way point is the 10th data point:

So the median age of people on your street is 35 years old. This is a little lower than the national average, but really not much. But there is something else that we can look at on this stem and leaf plot. Notice the 99 on this plot, it is far away from the rest of the data, we even skip an entire tens group before we get to 99. So it can be considered an outlier. An outlier is something that is considerably different from the rest of the group. We often take outliers out of our data sets. So now let's take out the 99 and see our new median:

Since there are 18 data points now the half way point lies between the 9th and 10th data point. The 9th data point is 25 and the 10th data point is 35. Half way between these two numbers is 30. So the new median age is 30 year old, which is quite a bit younger than the national median. So we could conclude that the median age of people on your street is lower than the national average.

Correlation

Now let's look a correlation, which is when we are seeing if two things affect each other. Let's say that you wanted to know if you drank more water the more popcorn that you ate at the movie theater. You went to the movie theater several times and measured how much popcorn you ate and water you consumed each time. You ended up with this data:

With this type of data it is easiest to see correlation by drawing a scatterplot, which is a graph with each of the data points plotted:

Then we draw a straight line which is about equal distance from each point, this is the line of best fit. Sometimes this can be difficult to draw, but simply use a ruler and it gets a lot easier.

Now let's look at this line, is does slope down, so that means there is a negative correlation between popcorn consumption and water consumption. In other words the more popcorn you eat the less water your drink. If it was sloped up then there would be a positive correlation (as one increases so does the other).

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