Quantitative data analysis techniques, such as descriptive statistics, exploratory data analysis, and confirmatory data analysis, use mathematical methods to interpret data and remove researcher bias. These techniques are used by researchers, businesses, and government agencies to make informed decisions. Descriptive statistics summarize data into specific groups, while exploratory data analysis supports a hypothesis and confirmatory data analysis tests the null hypothesis.
Data analysis techniques allow researchers to review collected data and make inferences or determinations from the information. Most techniques focus on applying quantitative techniques to review data. Some of the more popular quantitative data analysis techniques include descriptive statistics, exploratory data analysis, and confirmatory data analysis. The latter two involve the use of supporting or not supporting a predetermined hypothesis. Groups that may use these techniques include individual researchers, students, businesses, government agencies, and actuaries, among other parties that need information and data.
Quantitative data analysis attempts to remove researchers’ bias from the data collected. Heavy use of statistics, probability or other mathematical techniques allows individuals to use standard methods for interpreting data. When researchers attempt to use qualitative data analysis techniques – often based on an individual’s personal background, preferences, or research principles and reasoning – the data collected can be misread or interpreted incorrectly. Therefore, mathematical techniques are less susceptible to these errors and often more accepted by other individuals or researchers.
Descriptive statistical analysis separates or summarizes data into specific groups. Demographics is a common set of descriptive statistics. Researchers will collect information about a population’s age, gender, family size, income, job type, and other information. Another type of descriptive statistical analysis is a quarterback’s completion percentage when playing in a football game. If the quarterback completes six out of eight pass attempts, he has a 75 percent completion percentage. A flaw of this technique is the inability of statistics to provide additional information, such as the length of each pass.
Exploratory data analysis techniques often involve the use of box plots, histograms, Pareto charts, scatter plots, or stem and leaf plots. The main purpose of this technique is to support a hypothesis stated by a researcher. For example, a researcher might want to prove a hypothesis about the age range of owners who drive a specific type of car, such as a minivan. To test and support this hypothesis, the researcher will gather information and create a boxplot to determine the number of owners within its stated range. The statistics will provide information to support or not the hypothesis and show how many outliers there are in the collected data.
Confirmatory data analysis techniques are the opposite of exploratory techniques. In these tests, the researcher is trying to disprove the null hypothesis, which is a statement generally accepted as true by most individuals. To disprove the null hypothesis, the researcher will collect specific information related to the hypothesis and test the mean, variance, p values, and confidence intervals. The confidence interval demonstrated by the analysis of the confirmatory data will provide insight into how confident the researcher should be that the null hypothesis is true or false.
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