Statistical Analysis Meaning Methods Types Examples

What is Statistical Analysis?

Any given scenario can possess raw data sets; organizing and analyzing it by giving importance to each element present is vital. It helps deduce important values or future outcomes, makes the decision-making process less complex and provides a better view of the scenario.

Key Takeaways

Statistical analysis refers to the collection and analysis of data by forming statistics to derive meaningful information, which becomes useful in effective decision-making.Important types are descriptive analysis, inferential analysis, predictive analysis, prescriptive analysis, exploratory data analysis (EDA), and causal analysis.The five basic methods are mean, standard deviation, regression, hypothesis testing, and sample size determination.It is widely used by governments, businesses, banking entities, insurance companies, etc.

Statistical Analysis Explained

Statistical analysis is done on data sets, and the analysis process can create different output types from the input data. For example, the process can give summarized data, derive key values from the input, present input data characteristics, prove a null hypothesis, etc. The output type and format vary with the analysis method used.

It is highly used by governments and management professionals in the business world. It helps professionals like analysts to understand complex scenarios and the vast data associated with them. Statistical analysis data is important in politics, generating information that fuels political theory, campaign strategy, and policy development.

Various statistical analysis softwares forming subcategories of business intelligence tools are available to make the analysis process easy. Examples are Microsoft Excel (Analysis ToolpakAnalysis ToolpakExcel’s data analysis toolpak can be used by users to perform data analysis and other important calculations. It can be manually enabled from the addins section of the files tab by clicking on manage addins, and then checking analysis toolpak.read more), SPSS (Statistical Package for the Social Sciences), MATLAB, and SAS (Statistical Analysis Software).

Statistical Analysis Types

The two main types are descriptive statistics and inferential statisticsInferential StatisticsInferential statistics helps study a sample of data and make conclusions about its population. read more.

Descriptive statistics: It refers to collecting, organizing, analyzing, and summarizing data sets in an understandable format, like charts, graphs, and tables. It makes a large data set presentable and eliminates complexity to help analysts understand it. The format of the summary can be quantitative or visual.
Inferential statistics: Inferential statistics derive inference about a large population. It is based on the analysis and findings produced for sample data from the large population. Hence it makes the process cost-efficient and time-efficient. It generally includes the development of interval estimate, and points estimatePoints EstimateA point estimator is a statistical function used to derive an approximate single value which serves as a base to estimate the unknown population parameter among the sample data set of the whole population. It is considered to be an unbiased, consistent and most efficient technique.read more to conduct the analysis.

Some other types are the following:

Predictive analysis: This analysis is used to forecast future events based on past and present data. It uses machine learning tools, data mining, big data, predictive modeling, artificial intelligence, and simulations.
Prescriptive analysis: This analysis aims to prescribe the best possible outcome based on the assessed data. It helps make informed decisions and encourages efficient decision-making.
Exploratory data analysis (EDA): In statistics, this method studies data sets to highlight their major features, which is frequently done using statistical graphics and other data visualization approaches.
Causal analysis: It focuses on the cause and effect. In simple terms, it focuses on the crux of events occurring and the reason behind them; based on data analysis, it aids in understanding why something didn’t work out and failures in business and professional activities.

Methods of Statistical Analysis

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Mean

It is one of the simplest and most popular analysis methods easy to apply to data. The mean is the average value of data used in research. In statisticsStatisticsStatistics is the science behind identifying, collecting, organizing and summarizing, analyzing, interpreting, and finally, presenting such data, either qualitative or quantitative, which helps make better and effective decisions with relevance.read more, the term “meanMeanMean refers to the mathematical average calculated for two or more values. There are primarily two ways: arithmetic mean, where all the numbers are added and divided by their weight, and in geometric mean, we multiply the numbers together, take the Nth root and subtract it with one.read more” is commonly used to indicate average. It is calculated by adding the data values and dividing them by the total number of data points. Though it is a common method, it is advised to have other methods supporting it for effective decision-making.

Standard Deviation

Standard deviationStandard DeviationStandard deviation (SD) is a popular statistical tool represented by the Greek letter ‘σ’ to measure the variation or dispersion of a set of data values relative to its mean (average), thus interpreting the data’s reliability.read more is a common statistical analysis tool to determine the deviation of a set of values from the mean value. The standard deviation value will be low if the deviation from the mean is small and vice versa.

Regression

The regression method helps comprehend the relationship between two or more variables used in the analysis. It shows how one variable is dependent on the other and their inter effect on each other. There is simple linear regression using a single independent variableIndependent VariableIndependent variable is an object or a time period or a input value, changes to which are used to assess the impact on an output value (i.e. the end objective) that is measured in mathematical or statistical or financial modeling.read more to interpret the dependent variable and multiple linear regression using multiple independent variables to interpret the outcome.

Hypothesis Testing

The method tests the validity and authenticity of a hypothesis, outcome, or argument. Hypothesis testingHypothesis TestingHypothesis Testing is the statistical tool that helps measure the probability of the correctness of the hypothesis result derived after performing the hypothesis on the sample data. It confirms whether the primary hypothesis results derived were correct.read more is an assumption set at the beginning of the research; after the test is over and a result is obtained based on it, the belief can be either true or false. In addition, it can check whether the null hypothesisNull HypothesisNull hypothesis presumes that the sampled data and the population data have no difference or in simple words, it presumes that the claim made by the person on the data or population is the absolute truth and is always right. So, even if a sample is taken from the population, the result received from the study of the sample will come the same as the assumption.read more or alternative hypothesis is true.

Sample Size Determination

The technique derives a sample from the entire population, representing the total population. When there is a large data set, and the analysis gets challenging, a small sample is taken for study and research.

Example

In a class, there are nine students, and the table given below contains the result of their math tests. Using the analysis method – “mean” summarizes the entire data by finding the mean of marks scored by the class.

Mean = Sum of all data points / Number of data points

= 558/9

= 62

Hence, the mean of marks scored by nine students is 62.

What is Statistical Analysis?#

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Statistical Analysis Explained#

Statistical Analysis Types#

Methods of Statistical Analysis#

Mean#

Standard Deviation#

Regression#

Hypothesis Testing#

Sample Size Determination#

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