Statistical analysis is a powerful tool businesses and organizations use to make sense of data and guide their decisionmaking. There are different types of statistical analysis techniques that can be applied to a wide range of data, industries and applications. Knowing the different statistical analysis methods and how to use them can help you explore data, find patterns and discover trends in your market.
In this article, we define statistical analysis and discuss the different types with examples.
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What are statistical analysis techniques?
Statistical analysis, or statistics, involves collecting, organizing and analyzing data based on established principles to identify patterns and trends. It is a broad discipline with applications in academia, business, the social sciences, genetics, population studies, engineering and several other fields. Statistical analysis has several functions. You can use it to make predictions, perform simulations, create models, reduce risk and identify trends.
Thanks to improving technology, many organizations now have vast amounts of data on every aspect of their operations and markets. To make sense of this data, businesses rely on statistical analysis techniques to organize their data and turn this information into tools for making precise decisions and longterm forecasts. Statistical analysis allows owners of data to perform business intelligence functions that solidify their competitive advantage, improve efficiency and optimize resources for maximum returns on investments.
Main types of statistical analysis
There are three major types of statistical analysis:
Descriptive statistical analysis
Descriptive statistics is the simplest form of statistical analysis, using numbers to describe the qualities of a data set. It helps reduce large data sets into simple and more compact forms for easy interpretation. You can use descriptive statistics to summarize the data from a sample or represent a whole sample in a research population. Descriptive statistics uses data visualization tools such as tables, graphs and charts to make analysis and interpretation easier. However, descriptive statistics is not suitable for making conclusions. It can only represent data so you can apply more sophisticated statistical analysis tools to draw inferences.
Descriptive statistics can use measures of central tendency, which uses a single value to describe a group. Mean, median and mode are used to get the central value for a given data set. For example, you can use descriptive statistical analysis to find the average age of drivers with a ticket in a municipality. Descriptive statistics can also find the measure of spread. For example, you can find the age range of drivers with DUI and atfault car accidents in a state. Techniques used to find a measure of spread include range, variation and standard deviation.
Inferential statistical analysis
Inferential statistical analysis is used to make inferences or draw conclusions about a larger population based on findings from a sample group it. It can help researchers find distinctions among groups present within a sample. Inferential statistics are also used to validate generalizations made about a population from a sample due to its ability to account for errors in conclusions made about a segment of a larger group.
To perform inferential statistical analysis, researchers estimate the parameters of the population from the sample. They can also perform a test of statistical hypothesis to arrive at a confidence interval that validates or disproves the generalizations made from the sample.
Associational statistical analysis
Associational statistics is a tool researchers use to make predictions and find causation. They use it to find relationships among multiple variables. It is also used to determine whether researchers can make inferences and predictions about a data set from the characteristics of another set of data. Associational statistics is the most advanced type of statistical analysis and requires sophisticated software tools for performing highlevel mathematical calculations. To measure association, researchers use a wide range of coefficients of variation, including correlation and regression analysis.
Other types of statistical analysis
Below are four other types of statistical analysis:
Predictive analysis
Predictive analysis uses powerful statistical algorithms and machine learning tools to predict future events and behavior based on new and historical data trends. It relies on a wide range of probabilistic techniques such as data mining, big data, predictive modeling, artificial intelligence and simulations to guess what is likely to occur in the future.
Predictive analysis is a branch of business intelligence as many organizations with operations in marketing, sales, insurance and financial services rely on data to make longterm plans. It is important to note that predictive analysis can only make hypothetical forecasts and the quality of the predictions depends on the accuracy of the underlying data sets.
Prescriptive analysis
Prescriptive analysis helps organizations use data to guide their decisionmaking process. Companies can use tools such as graph analysis, algorithms, machine learning and simulation for this type of analysis. Prescriptive analysis helps businesses make the best choice from several alternative courses of action.
Exploratory data analysis
Exploratory data analysis is a technique data scientists use to identify patterns and trends in a data set. They can also use it to determine relationships among samples in a population, validate assumptions, test hypotheses and find missing data points. Companies can use exploratory data analysis to make insights based on data and validate data for errors.
Causal analysis
Causal analysis uses data to determine causation or why things happen the way they do. It is an integral part of quality assurance, accident investigation and other activities that aim to find the underlying factors that led to an event. Companies can use causal analysis to understand the reasons for an event and use this understanding to guide future decisions.
Statistical analysis process
There are five major steps involved in the statistical analysis process:
1. Data collection
The first step in statistical analysis is data collection. You can collect data through primary or secondary sources such as surveys, customer relationship management software, online quizzes, financial reports and marketing automation tools. To ensure the data is viable, you can choose data from a sample that’s representative of a population. For example, a company might collect data from previous customers to understand buyer behaviors.
2. Data organization
The next step after data collection is data organization. Also known as data cleaning, this stage involves identifying and removing duplicate data and inconsistencies that may prevent you from getting an accurate analysis. This step is important because it can help companies ensure their data and the conclusions they draw from the analysis are correct.
3. Data presentation
Data presentation is an extension of data cleaning, as it involves arranging the data for easy analysis. Here, you can use descriptive statistics tools to summarize the data. Data presentation can also help you determine the best way to present the data based on its arrangement.
4. Data analysis
Data analysis involves manipulating data sets to identify patterns, trends and relationships using statistical techniques, such as inferential and associational statistical analysis. You can use computer software like spreadsheets to automate this process and reduce the likelihood of human error in the statistical analysis process. This can allow you to analyze data efficiently.
5. Data interpretation
The last step is data interpretation, which provides conclusive results regarding the purpose of the analysis. After analysis, you can present the result as charts, reports, scorecards and dashboards to make it accessible to nonprofessionals. For example, the interpretation of the analysis of the impact of a 6,000worker factory on the crime rate in a small town with a population of 13,000 residents can show a declining rate of criminal activities. You may use a line graph to display this decline.
4 common statistical analysis methods
Here are four common methods for performing statistical analysis:
Mean
You can calculate the mean, or average, by finding the sum of a list of numbers and then dividing the answer by the number of items in the list. It is the simplest form of statistical analysis, allowing the user to determine the central point of a data set. The formula for calculating the mean is:
Mean = Set of numbers / Number of items in the set
Example: You can find the mean of the numbers 1, 2, 3, 4, 5, and 6 by first adding the numbers together, then dividing the answer from the first step by the number of figures in the list, which is six. The mean of the numbers is 3.5.
Standard deviation
Standard deviation (SD) is used to determine the dispersion of data points. It is a statistical analysis method that helps determine how the data spreads around the mean. A high standard deviation means the data disperses widely from the mean. A low standard deviation shows that most of the data are closer to the mean.
An application of SD is to test whether participants in a survey gave similar questions. If a large percentage of respondents’ answers are similar, it means you have a low standard deviation and you can apply their responses to a larger population. To calculate the standard deviation, use this formula:
σ2 = Σ(x − μ)2/n

σ represents the standard deviation

Σ represents the sum of the data

x represents the value of the dataset

μ represents the mean of the data

n represents the number of data points in the population
Example: You can calculate the standard deviation of the data set used in the mean calculation. The first step is to find the variance of the data set. To find variance, subtract each value in the data set from the mean, square the answer, add everything together and divide by the number of data points.
Variance = ((3.51)² + (3.52) ² + (3.53) ² + (3.54) ² + (3.55) ² + (3.56) ²) / 6
Variance = (6.25 + 2.25 + 0.25 + 0.25 + 2.25 + 6.25) / 6
Variance = 17.25/6 = 2.875
Next, you can calculate the square root of the variance to find the standard deviation of the data.
Standard deviation = √2.875 = 1.695
Regression
Regression is a statistical technique used to find a relationship between a dependent variable and an independent variable. It helps track how changes in one variable affect changes in another or the effect of one on the other. Regression can show whether the relationship between two variables is weak, strong or varies over a time interval. The regression formula is:
Y = a + b(x)

Y represents the independent variable, or the data used to predict the dependent variable

x represents the dependent variable which is the variable you want to measure

a represents the yintercept or the value of y when x equals zero

b represents the slope of the regression graph
Example: Find the dollar cost of maintaining a car driven for 40,000 miles if the cost of maintenance when there is no mileage on the car is $100. Take b as 0.02, so the cost of maintenance increases by $0.02 for every unit increase in miles driven.

Y = cost of maintaining the car

X = 40,000 miles

a = $100

b = $0.02
Y = $100 + 0.02(40,000)
Y = $900
This shows that mileage affects the maintenance costs of a car.
Hypothesis testing
Hypothesis testing is used to test if a conclusion is valid for a specific data set by comparing the data against a certain assumption. The result of the test can nullify the hypothesis, where it is called the null hypothesis or hypothesis 0. Anything that violates the null hypothesis is called the first hypothesis or hypothesis 1.
Example: From the regression calculation above, you want to test the hypothesis that mileage affects the maintenance costs of a car. To test the hypothesis, you claim mileage affects the maintenance costs of a car. Here, we reject the null hypothesis since the regression above shows that mileage influences car maintenance costs.
I hope you find this article helpful.
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