Data visualization – the fundamental details of the histogram
Description of the visual
The chosen visual is a histogram representation of the count of total movies starred by Michael Caine. The time period with an even consistency of 5 years is plotted on the X-axis, starting right from the first theatrical release of the movies. The vertical axis is used to scale the count of movies that were released within the respective period of five years from 1965 and earlier to 2010 and later. Being a visual representation of the same range of values, the bar sizes are used to draw the relation among the values at the specific intervals.
Purpose of the visual
The primary purpose of data visual made through a histogram is to bring out the entire pattern that exists within the observation distributions. This type of data visualization represents the distribution of a quantitative group range along with its frequency (Gevers & Stokman, 2004). In case the bins of the histogram are relational with the values of specific intervals, the line/bar sizes are used to compare the observation. In case the bins belong to a value range that is unequal, the area covered by the rectangles of the histogram shows the quantitative counts. This particular visual represents the count of movies starred by Michael Caine over consistent time periods of 5 years.
Data to be included in this visual
Making a histogram is a simple task if the data compilation is expertly handled. The set of continuous or discrete data is ideal for summarizing a histogram (Lindholm et. al., 2015). Numerical data that represents the data points of the mentioned value range must be clearly identified before the compilation. The intervals are compiled on the X-axis, while the frequency counts as per the complied intervals are presented in the Y-axis – shown by the height or area of the bars of the histogram.
Data to be excluded from this visual
Unlike the bar charts, the histograms are designed for the comparison of the count of the observations made upon a value range. The quantitative counts of the specific categories are given. Data from varying categories and no link to the quantitative count must be avoided. Another essential point to keep under consideration is that the bins must be sorted in a meaningful fashion to represent ordinal grouping (Geng et. al., 2011). Adding up the bins abruptly from varied categories may turn the visual useless and meaningless. It should not be used for the comparison of quantities for varied categories.
Misleading visuals – how to avoid them?
Wrong data has more impact than no data. The risks of incomplete data are even more when it comes to making misleading visuals (Lapray & Rebouillat, 2014). The visual makers often employ such techniques to manipulate the audience and keep them away from real facts. The best way to prevent the creation of misleading visuals is to double-check the data each time before a visual is created. In the case of a histogram, the equality of the value range must be identified to avoid further confusion. Also, the maker must have the knowledge and skills necessary to handle the task.