7 Types of Statistical Analysis (And Common Method Examples)
By Indeed Editorial Team
Published May 22, 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Businesses and organizations use statistical analysis as a business tool to help study and interpret data and help guide the decision-making process. You can choose to use various statistical analysis techniques depending on the type of data, its application, and the industry. Understanding the different kinds of statistical analysis techniques and how to use them can help you analyze data, identify patterns, and discover trends in the market. In this article, we explore 7 types of statistical analysis, explore the statistical analysis process, and explain common methods with examples.
7 types of statistical analysis
Here are 7 types of statistical analysis you can consider applying to your work:
1. Descriptive statistical analysis
Descriptive analysis is the most basic type of statistical analysis. It involves using numbers to explain the qualities of a data set. This helps you reduce complex data sets into a compact and simple form for straightforward interpretation. For example, you can use this technique to sum up the data from different samples or point out an entire sample in a research population. The descriptive analysis uses visual aids, such as graphs, tables, and charts, to help simplify data analysis and interpretation.
Descriptive statistical analysis represents the available data, so you can employ other advanced analytical techniques to draw inferences. In addition, descriptive analysis can apply measures of central tendencies, which implies using one value to describe a group. For example, you can use mean, mode, and median to derive the central value of a particular data set. You can also use descriptive analysis to derive the measure of spread. The techniques for finding the measure of spread are variation, range, and standard deviation.
2. Inferential statistical analysis
You can use this analysis technique to reach conclusions and draw inferences about a large sample size based on a smaller sample size's discoveries. For example, researchers use inferential statistical analysis to identify the differences among the sections in a sample. This technique accounts for mistakes in conclusions drawn about a section of a larger group.
You can use it to authenticate generalizations made about a large group from its samples. When using inferential statistical analysis, you estimate the population's parameters from its samples. You can also use a statistical hypothesis test to reach a confidence interval that confirms or invalidates the inferences made from the sections.
3. Associational statistical analysis
Researchers use the associational statistical analysis technique to make predictions and derive causation. You can also use it to find the relationship between multiple variables. Associational analysis helps determine if you can make predictions or inferences about a data set from the qualities of another set. This analysis technique is the most complex form of statistical analysis, and it requires advanced software tools to perform advanced mathematical calculations. Researchers use various coefficients of variation, such as regression analysis and correlation, for measuring the association between data sets.
4. Predictive analysis
This analysis technique uses powerful machine learning tools and advanced statistical algorithms to forecast future behaviours and events based on historical and new data trends. It applies various probabilistic methods, such as big data, data mining, simulations, artificial intelligence, and predictive modelling to predict what may occur in the future. Predictive analysis is an aspect of business intelligence that companies in the sales, marketing, financial, and insurance industries employ to help them analyze data for long-term planning. Still, the predictions in this technique are hypothetical and largely depend on the validity of the underlying data sets.
5. Prescriptive analysis
This technique allows companies to use data to inform their decision-making process. When conducting prescriptive analysis, you can use various tools, such as algorithms, graph analysis, simulations, and machine learning. This technique also helps you select the best option from multiple alternatives.
6. Exploratory data analysis
Data scientists use the exploratory data analysis technique to discover trends and patterns within a data set. You can also use this technique to validate assumptions, derive relationships between sections in a large sample, locate missing data points, and test hypotheses. In addition, various organizations use exploratory data analysis to gain insights based on the available data and authenticate data for errors.
7. Causal analysis
The causal analysis technique determines causation or why events happen with data. This analysis is an essential part of accident investigation, quality assurance, and different activities to discover the underlying factors of a particular occurrence. You can use the discoveries from a causal analysis to understand hazards and guide future decisions to prevent similar events.
Statistical analysis process
Here's a list of steps you can follow when conducting statistical analysis:
You can gather data from primary and secondary sources, such as online quizzes, customer relationship management software (CRM) surveys, marketing automation tools, and financial reports. You can also select data from a section that represents the entire population for access to viable data. For instance, you can get feedback from existing or previous customers to study consumer behaviour.
After gathering data, the next step is to organize the data, also known as data cleaning. Data organization involves finding and removing inconsistencies and duplicates that may disrupt your analysis. Data cleaning is essential because it ensures that your analysis is correct and helps you reach accurate conclusions.
This step is an extension of organizing or cleaning your data. It involves arranging and presenting the data for easy analysis. You may use the tools for descriptive analysis to help you summarize the data. Arranging your data can help you choose the best way to present it.
In this step, you use various statistical techniques, such as associational and inferential methods to manipulate your data sets to find relationships, discover trends, and identify patterns. You can also automate the process with software programs like spreadsheets. Automating the process can help efficiently complete the process and reduce human errors.
Data interpretation ensures you get conclusive results after conducting your analysis. You can display the results of your analysis as reports, charts, dashboards, or scorecards to allow others to read and evaluate it. For instance, the interpretation of an analysis of the impact of subsidized importation tariffs and new taxation laws on the manufacturing sector can show an increase in profit margins and higher productivity. You can show the increase with a line graph.
Common statistical analysis methods
Some common methods of performing statistical analysis include:
You can find the average of a data set by calculating the sum of the values and dividing the answer by the number of values in the set. The mean is the simplest statistical analysis formula, and it helps you find the central point of a set. To calculate the mean, you can use this formula:
Mean = Sum of values / number of values
Example: Suppose you want to find the mean of 4, 6, 7, 9, 10, and 12. First, add the numbers together, which gives you 48. Then divide the answer by the number of values in the set, 6. The mean is 48/6, which is 8.
You can use standard deviation to find the dispersion of data points. It helps you find how data spread around the mean (average). For instance, a high standard deviation implies that the data varies widely from the average. Conversely, a low standard deviation suggests that most of the values in the set are close to the average. The formula for calculating standard deviation is:
σ2 = Σ(x − μ)2/n
Example: Suppose you want to find the standard deviation of the values in the set used for the mean calculation. In that case, first, calculate the variance of the set. You can calculate the variance by subtracting each value in the set from the mean, squaring the answers, adding them together, and dividing the answer by the number of values. After calculating the variance, you can derive the standard deviation by finding the square root of the variance.
Variance = ((8-4)2 + (8-6)2 + (8-7)2 + (8-9)2 + (8-10)2 + (8 - 12)2) / 6
Variance = (16 + 4 + 1 + 1 + 4 + 16) / 6
Variance = 42/6 = 7
Standard deviation = √7 = 2.646
You can use hypothesis testing to validate the conclusion for a particular data set by comparing that value against your hypothesis. The results may nullify the assumption and give you hypothesis 0 or a null hypothesis. You refer to any value that violates the null hypothesis as hypothesis 1 or the first hypothesis.
Example: Suppose you have a hypothesis that customers within the age group of 20 to 30 conduct business online more than they do physically. First, state your null hypothesis, which is what may happen if the hypothesis is wrong. In this case, the null hypothesis is that people in the age group 20-30 conduct more business in person. Then you can conduct a survey to determine if your hypothesis is correct or not.
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