Why descriptive statistics

There are several graphical and pictorial methods that enhance researchers' understanding of individual variables and the relationships between variables. Graphical and pictorial methods provide a visual representation of the data.

Some of these methods include:. Each value of a variable is displayed along the bottom of a histogram, and a bar is drawn for each value. Display the relationship between two quantitative or numeric variables by plotting one variable against the value of another variable.

For example, one axis of a scatter plot could represent height and the other could represent weight. Each person in the data would receive one data point on the scatter plot that corresponds to his or her height and weight. A GIS is a computer system capable of capturing, storing, analyzing, and displaying geographically referenced information; that is, data identified according to location. Display networks of relationships among variables, enabling researchers to identify the nature of relationships that would otherwise be too complex to conceptualize.

Graphical Analytic Techniques. Geographic Information Systems. Measures of central tendency are the most basic and, often, the most informative description of a population's characteristics. They describe the "average" member of the population of interest. There are three measures of central tendency:. Mean -- the sum of a variable's values divided by the total number of values Median -- the middle value of a variable Mode -- the value that occurs most often.

The scatter plot shows the hours of sleep needed per day by age, Source. Recommend Blog: Introduction to Bayesian Statistics. High degree of objectivity and neutrality of the researchers are one of the main advantages of Descriptive Analysis. Descriptive analysis is considered to be more vast than other quantitative methods and provide a broader picture of an event or phenomenon. It can use any number of variables or even a single number of variables to conduct a descriptive research.

This type of analysis is considered as a better method for collecting information that describes relationships as natural and exhibits the world as it exists. This reason makes this analysis very real and close to humanity as all the trends are made after research about the real-life behaviour of the data.

It is considered useful for identifying variables and new hypotheses which can be further analyzed through experimental and inferential studies. It is considered useful because the margin for error is very less as we are taking the trends straight from the data properties. This type of study gives the researcher the flexibility to use both quantitative and qualitative data in order to discover the properties of the population. For example, researchers can use both case study which is a qualitative analysis and correlation analysis to describe a phenomena in its own way.

Using the case studies for describing people, events, institutions enables the researcher to understand the behavior and pattern of the concerned set to its maximum potential.

In the case of surveys which consist of one of the main types of Descriptive Analysis, the researcher tends to gather data points from a relatively large number of samples unlike experimental studies that generally need smaller samples.

This is an out and out advantage of the survey method over other descriptive methods that it enables researchers to study larger groups of individuals with ease. If the surveys are properly administered, it gives a broader and neater description of the unit under research.

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Descriptive statistics, in short, help describe and understand the features of a specific data set by giving short summaries about the sample and measures of the data. The most recognized types of descriptive statistics are measures of center: the mean , median , and mode , which are used at almost all levels of math and statistics.

The mean, or the average, is calculated by adding all the figures within the data set and then dividing by the number of figures within the set. For example, the sum of the following data set is 2, 3, 4, 5, 6.

The mode of a data set is the value appearing most often, and the median is the figure situated in the middle of the data set. It is the figure separating the higher figures from the lower figures within a data set. However, there are less common types of descriptive statistics that are still very important.

People use descriptive statistics to repurpose hard-to-understand quantitative insights across a large data set into bite-sized descriptions. A student's grade point average GPA , for example, provides a good understanding of descriptive statistics. The idea of a GPA is that it takes data points from a wide range of exams, classes, and grades, and averages them together to provide a general understanding of a student's overall academic performance.

A student's personal GPA reflects their mean academic performance. All descriptive statistics are either measures of central tendency or measures of variability , also known as measures of dispersion.

Measures of central tendency focus on the average or middle values of data sets, whereas measures of variability focus on the dispersion of data. These two measures use graphs, tables and general discussions to help people understand the meaning of the analyzed data. Measures of central tendency describe the center position of a distribution for a data set.

A person analyzes the frequency of each data point in the distribution and describes it using the mean, median, or mode, which measures the most common patterns of the analyzed data set.

Measures of variability or the measures of spread aid in analyzing how dispersed the distribution is for a set of data. For example, while the measures of central tendency may give a person the average of a data set, it does not describe how the data is distributed within the set.

So while the average of the data maybe 65 out of , there can still be data points at both 1 and Measures of variability help communicate this by describing the shape and spread of the data set.

The Mean or average is probably the most commonly used method of describing central tendency. To compute the mean all you do is add up all the values and divide by the number of values. For example, the mean or average quiz score is determined by summing all the scores and dividing by the number of students taking the exam. For example, consider the test score values:. The Median is the score found at the exact middle of the set of values.

One way to compute the median is to list all scores in numerical order, and then locate the score in the center of the sample. For example, if there are scores in the list, score would be the median.

If we order the 8 scores shown above, we would get:. There are 8 scores and score 4 and 5 represent the halfway point. Since both of these scores are 20 , the median is If the two middle scores had different values, you would have to interpolate to determine the median. The Mode is the most frequently occurring value in the set of scores. To determine the mode, you might again order the scores as shown above, and then count each one. The most frequently occurring value is the mode.

In our example, the value 15 occurs three times and is the model. In some distributions there is more than one modal value. For instance, in a bimodal distribution there are two values that occur most frequently. Notice that for the same set of 8 scores we got three different values If the distribution is truly normal i. Dispersion refers to the spread of the values around the central tendency. There are two common measures of dispersion, the range and the standard deviation.

The range is simply the highest value minus the lowest value. The Standard Deviation is a more accurate and detailed estimate of dispersion because an outlier can greatly exaggerate the range as was true in this example where the single outlier value of 36 stands apart from the rest of the values.

The Standard Deviation shows the relation that set of scores has to the mean of the sample. Again lets take the set of scores:. We know from above that the mean is So, the differences from the mean are:.