Displaying Statistical Data

Methods of Displaying Statistical Data

The most basic way of presenting statistical data is through text. There are many benefits to using this format. You are able to go into more depth to describe and contextualize your findings. You can explain what each variable is and how other elements are controlled for. However, displaying statistical data offers a visual element to interpreting data. Especially if you have received a lot of information, displaying statistical data can help you mentally organize your findings.

However, by using analysis and displaying statistical data, we can begin to fill in some of the gaps. There are two primary methods of displaying statistical data: tabular form and graphical form.

Displaying Numerical Data Statistics

Let's begin by taking a look at a few basic ways of displaying numerical data statistics.

Tables

One of the oldest ways of displaying numerical data statistics is through tables. This may be due to the fact that tables are easy to understand. Generally, if you know how to read, you can understand the information on a table without any prior training.

Tables are also the most appropriate option for displaying individual information. This way all the information can be seen rather than grouped together or generalized like many graphical forms of displaying statistical data. For example, if the result for one data point is 65.489, you can simply write that into the table. However, in other forms of displaying statistical data, you may have to round that number to 65 or, you may not even see what the number is at all.

Tables are also great for comparing quantitative information between different variables. Take the following as an example.

Table comparing variables, commons.wikimedia.org

A common type of table is called a frequency table. Frequency is simply how often a specific finding occurs. There are several types of frequencies a frequency table can display as well: relative frequency and……

Frequency table, commons.wikimedia.org

While tables can be incredibly useful in presenting a large number of numerical data statistics, there are downsides. One major downside to using tables is that it can take a long time to interpret the data. Sometimes, there is so much information, it can be difficult to see any data trends that may be occurring. For this reason, graphical forms of displaying statistical data are especially useful.

Pie Chart

Another great way of visually displaying statistical data is by using a pie chart. A pie chart is generally used to nominal data (or, in other words, different categories of data). Usually, a pie chart should only be used to represent only a small number of categories but can still be a great way to summarize a large amount of data.

The one downfall of a pie chart is that there is no way to know the results of individual data. Results from each category are grouped together to form a broad picture of the results. Also, pie charts do not offer any information regarding outliers, means, medians, or any other summary information that may aid in analyzing the results of your research.

Graphical Display of Statistical Data

Graphs come in handy when displaying statistical data because they often simplify complex information by using images and highlighting data trends. They are useful for explaining, summarizing, and exploring the quantitative data we collect in our research. Graphical displays of statistical data can also come in handy because they can present large amounts of data. If you have just collected thousands of data points from your research, it would be unrealistic to display each point on a table.

Also, finding a way to categorize the information onto a table may erase any statistical trends you are trying to uncover. Let’s take a look at a few types of graphical displays of statistical data including a box and whiskers chart, bar graphs and histograms, and scatter plots.

Box and Whisker Plot

A very basic way to graphically display statistical data is with a box and whisker plot. When analyzing a data set, one of the first things researchers will do is summarize the data. A box and whisker plot includes a five-number summary:

• Minimum: the smallest value in the data set

• First quartile: the lower 25% of the data set

• Medium: the middle value in the data set

• Third quartile: the upper 25% of the data set

• Maximum: the largest value in the data set

The rectangle represents the first quartile, median, and third quartile. The lines or whiskers extend to the minimum and maximum values in the data set. Sometimes, there you may get results that are way outside of the other results in the data set. These are called outliers and are usually represented by a single point outside of the box and whisker plot and are not considered the minimum or maximum value.

Box and whisker plot, commons.wikimedia.org

Box and whisker plots are a great way to visually display a summary of your findings. It can also be useful in comparing multiple data sets, evaluating how one data set changes over time, or seeing how the data set has changed before and after an experiment.

Bar Graph and Histogram

Bar graphs and histograms are also commonly used methods of displaying statistical data.

The bar height or length indicates the amount of information in that category. You can use any measurement parameters you'd like such as frequency or mean. Bar graphs and histograms are very similar but there are a few ways in which they differ? For one, histograms show the value of a range of categories or data points while bar graphs show just one. Additionally, the bars on bar graphs are separated while the bars on histograms are always touching.

Bar graph, commons.wikimedia.orgHistogram, commons.wikimedia.org

Bar graphs and histograms can be especially helpful in analyzing data because they can visually show trends within the data that other graphs such as pie charts cannot.

Scatter Plot

Finally, one of the most effective ways of displaying statistical data is with a scatter plot. Scatter plots allow you to display all the values in your data set represented by a single dot on the graph. The x- and y-axes represent the two variables you are investigating. Once all the data is plotted, you will be able to see if there is an association between those two variables. This association is commonly referred to as correlation.

In psychology, researchers are always looking for a correlation between variables. Finding a correlation between variables can lead to important discoveries and a better understanding of the human mind and behavior.

If the correlation isn't immediately clear, a correlation regression line can be calculated which can then be plotted to show correlation. It is important to note, however, that there may not always be a correlation between variables.

Displays Statistics of Data That Varies Over Time

Sometimes, researchers want to observe how a variable changes over time. The best way to visually display statistics of data that varies over time is by using a line graph. Line graphs use a single dot to indicate a data point. Then, a straight line is drawn to connect each dot on the graph. This will visually show any increases or decreases (if any) between each time interval. Line graphs also show any broader trends over an extended amount of time. The x-axis is usually time and the y-axis is the variable you are investigating.

Line graph, commons.wikimedia.org

While a line graph is the best way to show changes over time, it can also be helpful in showing other types of continuous variables such as distance. Line graphs are usually best for smaller data sets.

Best Way to Display Statistical Data

Now that we've briefly discussed the ways of displaying statistical data, how do you decide which methods are the best way to display statistical data? Several factors should be considered while making this decision including:

• simplicity of presentation

• the data format

• the method of analysis you plan to use

• the information you want to emphasize

• the amount of information in your data set