How do scientists summarize their findings with visual aids? In this lesson, explore the different types of tables, charts and graphs that scientists use. Learn to read these effectively as a preview to your science studies.

## Representation of Scientific Work

The scientific method is a set of procedures that scientists use to learn about the world.

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Scientists generally follow this method, but they also compete and collaborate on their work in order to reach a consensus within the scientific community. Sometimes, a scientist’s work can take years to complete, but he still has to communicate his ideas to others. How do scientists summarize their findings so that other scientists can understand it in a matter of minutes? The answer is in the tables, graphs and charts that illustrate their data. In this lesson, we’ll look at the basic types of charts and learn how to read them effectively.

## Data in Tables & Line Graphs

Let’s say that you were a scientist who wanted to investigate how fast a sunflower grows compared with a daisy. You’d plant a sunflower seed and a daisy seed, grow them both with plenty of sunshine and water and measure the height of each plant every day. To keep all of your measurements organized, you’d likely use a table. Most scientists use tables as a way of arranging information into vertical columns and horizontal rows. Tables are useful for data that describes two different factors, or variables, because it reads in two directions. In your plant growth experiment, you’d collect data about two different things: time and the height of the plants.

Let’s say you’d record time measurements in the first column and the measurements for plant height in the second column. To be clear, you’d specify which units you’re using for each variable. This is what a basic table looks like for most scientific experiments.The two variables that you’re recording are different, in that one variable is dependent upon the other.

Since the height of the plant changes due to the passage of time, we say that height is dependent on time. So in this case, height is called the dependent variable. A dependent variable is the factor being measured in an experiment, which changes in response to the independent variable. The factor that’s not dependent on anything is time. That is, nothing is going to change how time elapses during the experiment. Therefore, we call time the independent variable.

It is the factor that is considered to be constant during an experiment, which affects the dependent variable. In tables, the independent variable is usually listed in the first column and the dependent variable is listed to the right.But remember, you’ll need to add a third column to collect your data about the daisy. Every day, you’ll make two measurements: one of the height of the sunflower plant and one of the height of the daisy plant. So you should be filling up one row for every day in your experiment.

By the time the experiment’s done, your table should be full of useful data.This table is a great way to keep your data organized. But it’s not so easy to draw any conclusions by taking a quick glance. A better way to look at this information would be to transform it into a line graph. A line graph depicts the relationship between the dependent and independent variables.

Each variable is plotted along one of the two axes in the graph. The x-axis is the horizontal axis, which usually plots the independent variable. Since your independent variable is time, then you’d plot time in days along the x-axis. The other axis is called the y-axis.

This is the vertical axis that usually plots the dependent variable. So your dependent variable is plant height. You’re actually going to have two lines on the graph: one to show the growth of the sunflower plant, and one to show the growth of the daisy.

Let’s make the sunflower line orange and the daisy line blue. You’ll need to add a key to the bottom to show which line describes the growth of which plant.As you’re transferring your data, you’ll need to be sure to plot all the numbers correctly. The first measurement for the sunflower says that it was half a centimeter tall on the first day. So you’d find the first day on the x-axis, and draw a dot that lines up with ‘0.

5 centimeters’ on the y-axis. You’d continue the same way for all of your data in the second column. Then, to plot the growth of the daisy, you’d use the data from the third column and draw the line in blue.

Bar charts compare amounts of something between unrelated groups.
basal metabolic rate to the time that has passed after eating a meal.

They’ve put time on the x-axis (as the independent variable), just like you did in your plant growth chart. The dependent variable here is the percentage of basal metabolic rate. Um, do you know what basal metabolic rate is? Well, guess what? You don’t have to! To interpret this chart, you just need to see that proteins, carbohydrates and lipids account for different percentages that fluctuate over several hours. It looks like carbohydrates account for the highest percentage in the first 45 minutes. But after that, proteins win out and take the highest percentage. Proteins peak at about 30 percent, and lipids stay fairly constant as the lowest contributor. You see how easy it can be to read a line graph? I don’t even have to understand the terminology.

I only need to use the labels and the x- and y-axes to interpret the relationship between these two variables.

## Bar Charts, Pie Charts & Flow Charts

Line graphs aren’t the only way that scientists represent their data. Different types of charts may be better suited for the type of information a scientist wants to show. All charts are visual representations of data that use symbols to indicate information. In a line graph, the data is represented in lines. But data can also be represented as bars in a bar chart or wedges in a pie chart. Let’s take a closer look at these two charts.

### Bar Charts

In a bar chart, information is represented in rectangular bars that are proportional in length to the data. Here’s a typical bar chart depicting numerical values for several geographic regions. Without even knowing what the values represent, we can tell from the relative heights of the bars that the United States has the most, Japan has the least and South Africa and Europe are somewhere in the middle. We typically use a bar chart when we want to compare the amounts of something between unrelated groups.

### Pie Charts

In a pie chart, information is represented in sections of a circle that are proportional in width to the data.

In this pie chart, we see two very large slices of pie, a handful of medium slices and many small slices. Pie charts are used when we want to show how different groups make up the percentages of something. So, unlike bar charts, pie charts have a defined limit of 100%, which is the entirety of the circle. If one slice took up half the circle, then we’d know that it represented 50%. This particular pie chart represents 100% of mammal species. Each slice represents a different family of mammals.

From the key, we can determine that the largest slice represents the family Rodentia. So that means rodents make up the largest family of mammal species.

### Flow Charts

Line graphs, bar charts and pie charts are all good for representing numerical data. But other charts can be used to illustrate relationships between concepts.

Take a flow chart, for example. A flow chart is a diagram that shows how concepts, or steps in a process, are related to one another. This flow chart is showing us a negative feedback loop for glucocorticoids. Do you know what glucocorticoids are? Neither do I! But according to the arrows, they probably come from the adrenal cortex, which is somehow acted upon by something called ACTH from the pituitary gland. Again, I don’t need to know the terminology. Flowcharts are designed to oversimplify difficult concepts. This makes them a great tool for summarizing, memorizing and studying science.

Here’s another flowchart which is really a food web. It shows how different organisms are related to each other based on who eats who. From this diagram, we can see that the bald eagle likes to eat sea ducks and large fish, since there are arrows pointing from these organisms to the eagle. The sea ducks, in turn, eat bivalves and benthic invertebrates. But bivalves are not only eaten by sea ducks. They are also eaten by tundra swans and herbivorous ducks.

I could go on, but I don’t need to. This food web tells me everything I need to know about who eats who in this environment.

As a student learning science, you will end up studying more complicated flow charts – like this one depicting the citric acid cycle.

Don’t get intimidated by intricate charts like this. They work just the same way as the flowcharts we saw before. You’ve got labels and symbols describing each step in the process. You’ve got arrows showing how one step proceeds toward the next. Take your time with complex charts like this, and use the legends and keys you are given. Just remember that the purpose of any graph or chart is to make detailed information easier to understand.

## Lesson Summary

The information that scientists accumulate in their studies is often too complicated to be summarized in words.

To communicate their findings to the rest of the community, scientists use tables, graphs and other charts. Tables organize data into rows and columns, which can be transformed into line graphs. Bar charts compare data between unrelated groups, while pie charts compare data as relative percentages.

For non-numerical information, scientists organize concepts into flow charts, which can show relationships between concepts or the order of steps in a process. Tables, charts and graphs are designed to summarize information and make it easier to understand scientific ideas.

## Learning Outcomes

After you’re done with the lesson, you should be able to:

• Describe ways scientists compare, organize and display information
• Recognize the key types of graphs and charts, including line graphs, bar charts, pie charts and flow charts
• Identify the x axis, y axis and experiment variables