How does graphing help scientists

Graphs should clearly communicate a message to your audience. You should keep this message in mind when creating and formatting your graph. As a general rule, you should ensure that all of your figures for scientific articles or lab reports can be easily interpreted when printed in black and white. Colour can be used if your audience is likely to view the graph in colour i.

Pie charts are rarely used in scientific articles, but they can be useful when communicating with the public.

You should check the requirements of your assignment with your lecturer for guidance on how to display your data. Continuous variable: Continuous variables are numeric measurements or observations that can include any number of values within a certain range.

Discrete variable: Discrete variables are measured as whole units. Categorical variable: Categorical variables describe a quality or a characteristic E. Colour, species, sex, blood type. Independent variable: The independent variable is the variable which you control or manipulate in your experiment, or the variable that you think will affect the dependent variable. Independent variables are placed on the x-axis of a graph. Dependent or response variable: The dependent variable is the variable you think will be influenced by the independent variable.

Changes in the dependent variable are observed or measured in relation to changes in the independent variable. The independent variable is the amount of light exposure and the dependent variable is the rate of growth. Sometimes a table will be more appropriate for displaying your data. Tables are great for displaying multiple variables, specific values, and comparing categories. A table will often require an audience to look up specific information to understand the data.

Therefore, you should ensure your table is presented in a neat and logical manner. Similar to graphs, you need to consider the message in your data that you want to communicate to your audience. You may need to perform a statistical analysis on your data or summarise your results before adding the information to a table.

For large tables, you may need to shade alternate rows or highlight important details by using a bold font to allow your audience to read the table efficiently. All of the tables and graphs that you create for scientific articles and lab reports will require a legend. The concise description in a table or figure legend should convey the key message of the table or graph to your audience without having to read the full article.

This module focuses on graphs and tables for use in scientific articles and lab reports. If you are designing a graph for a presentation or poster, you should refer to the relevant module for further design guidelines.

Figure number Figure 1 or Fig. The figure number is used to allow your audience to find the figure you have referred to in your text. A descriptive figure title briefly describes what the figure is displaying but lets the reader identify any trends or relationships, or is guided by the text you include in the results section. An assertive title can be used to identify a specific trend found in a graph or highlight the key message of a diagram.

You should use the graph generated from your data to see if your hypothesis is supported. Which is a good hypothesis for this tadpole experiment? Students who consume large amounts of caffeine while studying will have lower exam scores than those who consume less caffeine. The experimental group should get the pain reliever and the control group should get a placebo.

The hypothesis is rejected, and we search for a new interpretation, an new hypothesis that supports the experimental data. Which is an alternate, nonpredictive hypothesis for an experiment about tadpole diets?

Tadpole size will differ if groups of tadpoles are fed different diets. By the end of the experiment half of our sowbugs were on the dry side and half were on the moist side. Pill bugs, sometimes also referred to as roly-pollies, primarily consume plant matter that is either decaying or is already dead and decomposed. Their preferred foods are soft decaying plants like grasses and leaves, but they may also eat mulch used in landscaping around the house.

Several essential oils have shown to be effective against troublesome pests including pennyroyal oil, peppermint oil, eucalyptus oil, cinnamon oil, citrus oil, citronella oil, rosemary oil, oregano oil and tea tree oil. Oils of cinnamon and oregano are particularly effective against pill bugs. Females have growths on some legs that resemble leaves.

Crustaceans — including pill bugs — absorb oxygen through their gills. Gills only function when they are wet, so pill bugs must inhabit places in which the air holds a lot of water. When the ambient humidity is high — such as after rainy weather — the pill bugs are able to move about in the open. The data relates to the background information because pillbugs can be found in dark areas, but in our experiment the pillbugs prefered the light instead of the dark.

The pillbugs prefer the light instead of the dark based on our observations and data. Although these creatures are common, you rarely see them during the day because they prefer dark, moist places — under rocks, boards, bricks, trash, decaying vegetation, or just beneath the soil surface. Mulches, grass clippings, and leaf litter often provide the decaying organic matter these creatures need to survive.

Our findings: Pill bugs are no longer interested in our plants — prior to our application of coffee grounds any new transplant was mauled and gnawed to the ground within 24 hours of planting. Sow bugs and pill bugs are crustaceans, just like shrimps, lobsters and crabs. They breathe with gills, so they need moisture in order to respire. Because of this you should have seen that most of the sow bugs and pill bugs spent more time in the damp rather than the dry soil environment. When hatched, they are fully formed but barely visible due to their small size.

Roly polies develop into adults in about a year. Data lie at the heart of any scientific endeavor. Scientists in different fields collect data in many different forms, from the magnitude and location of earthquakes , to the length of finch beaks, to the concentration of carbon dioxide in the atmosphere and so on.

Visual representations of scientific data have been used for centuries — in the s, for example, Copernicus drew schematic sketches of planetary orbits around the sun — but the visual presentation of numerical data in the form of graphs is a more recent development. In , William Playfair, a Scottish economist, published The Commercial and Political Atlas , which contained a variety of economic statistics presented in graphs. Among these was the image shown in Figure 1, a graph comparing exports from England with imports into England from Denmark and Norway from to Playfair, Incidentally, William Playfair was the brother of John Playfair, the geologist who elucidated James Hutton 's fundamental work on geological processes to the broader public.

Playfair's graph displayed a powerful message very succinctly. The graph shows time on the horizontal x axis and money in English pounds on the vertical y axis. The yellow line shows the monetary value of imports to England from Denmark and Norway; the red line shows the monetary value of exports to Denmark and Norway from England.

Although a table of numerical data would show the same information, it would not be immediately apparent that something important happened in about England began exporting more than it imported, placing the "balance in favour of England.

Graphs and figures quickly became standard components of science and scientific communication, and the use of graphs has increased dramatically in scientific journals in recent years, almost doubling from an average of 35 graphs per journal issue to more than 60 between and Zacks et al.

This increase has been attributed to a number of causes, including the use of computer software programs that make producing graphs easy, as well as the production of increasingly large and complex datasets that require visualization to be interpreted.

Graphs are not the only form of visualized data , however — maps, satellite imagery, animations, and more specialized images like atomic orbital depictions are also composed of data, and have also become more common.

Creating, using, and reading visual forms of data is just one type of data analysis and interpretation see our Data Analysis and Interpretation module , but it is ubiquitous throughout all fields and methods of scientific investigation. The majority of graphs published in scientific journals relate two variables. Although many other kinds of graphs exist, knowing how to fully interpret a two-variable graph can not only help anyone decipher the vast majority of graphs in the scientific literature but also offers a starting point for examining more complex graphs.

As an example, imagine trying to identify any long-term trends in the data table that follows of atmospheric carbon dioxide concentrations taken over several years at Mauna Loa Table 1; click on the excerpt below to see the complete data table. The variables are straightforward — time in months in the top row of the table, years in the far left column of the table, and carbon dioxide CO 2 concentrations within the individual table cells.

Yet, it is challenging for most people to make sense of that much numerical information. You would have to look carefully at the entire table to see any trends. But if we take the exact same data and plot it on a graph, this is what it looks like Figure 2 :. Describing the graph: The x-axis shows the variable of time in units of years, and the y-axis shows the range of the variable of CO 2 concentration in units of parts per million ppm.

The dots are individual measurements of concentrations — the numbers shown in Table 1. Thus, the graph is showing us the change in atmospheric CO 2 concentrations over time. Describing the data and trends: The line connects consecutive measurements, making it easier to see both the short- and long-term trends within the data.

On the graph, it is easy to see that the concentration of atmospheric CO 2 steadily rose over time, from a low of about ppm in to a current level of about ppm. Within that long-term trend, it's also easy to see that there are short-term, annual cycles of about 5 ppm. Making interpretations: On the graph, scientists can derive additional information from the numerical data, such as how fast CO 2 concentration is rising.

This rate can be determined by calculating the slope of the long-term trend in the numerical data, and seeing this rate on a graph makes it easily apparent. While a keen observer may have been able to pick out of the table the increase in CO 2 concentrations over the five decades provided, it would be difficult for even a highly trained scientist to note the yearly cycling in atmospheric CO 2 in the numerical data — a feature elegantly demonstrated in the sawtooth pattern of the line.

Putting data into a visual format is one step in data analysis and interpretation , and well-designed graphs can help scientists interpret their data. Interpretation involves explaining why there is a long-term rise in atmospheric CO 2 concentrations on top of an annual fluctuation, thus moving beyond the graph itself to put the data into context.

Seeing the regular and repeating cycle of about 5 ppm, scientists realized that this fluctuation must be related to natural changes on the planet due to seasonal plant activity. Visual representation of these data also helped scientists to realize that the increase in CO 2 concentrations over the five decades shown occurs in parallel with the industrial revolution and thus are almost certainly related to the growing number of human activities that release CO 2 IPCC, It is important to note that neither one of these trends the long-term rise or the annual cycling nor the interpretation can be seen in a single measurement or data point.

That's one reason why you almost never hear scientists use the singular of the word data — datum. Imagine just one point on a graph. You could draw a trend line going through it in any direction. Rigorous scientific practice requires multiple data points to make a clear interpretation, and a graph can be critical not only in showing the data themselves, but in demonstrating on how much data a scientist is basing his or her interpretation.

We just followed a short, logical process to extract a lot of information from this graph. Although an infinite variety of data can appear in graphical form, this same procedure can apply when reading any kind of graph. To reiterate:.