What is statistical analysis?
Posted: Sun Jan 19, 2025 7:05 am
Statistical analysis is a fundamental approach to data science, significantly aiding the process of data analysis and the discovery of patterns, trends, and market predictions. This analysis can be applied in a variety of contexts, supporting the end-to-end data analysis process . This tool is increasingly used by businesses seeking to guide their strategies on real data from a complete and in-depth data analysis process.
Continue reading this article and understand what statistical analysis is, its importance, main types and how to apply them to projects.
Statistical analysis aims to support the process of collecting, analyzing and interpreting data , creating data models capable of raising hypotheses, identifying patterns and trends and making why buy overseas chinese uk directory predictions. This field of data science works with large amounts of data, collecting information from different sources to support the results and their interpretations.
By collecting and cleaning data sets, it is possible to transform them into useful and valuable insights for the business.
This process is based on mathematical methods that, through several components, enable the creation of robust and in-depth data models , capable of extracting insights to support a business's informed decision-making .
Thus, statistical analysis can be applied to different contexts and businesses, such as technology, business, economics, social sciences, medicine, etc.
How important are statistical analyses?
In a context of growing digital transformation, where there is so much availability of raw data from various sources and where companies still do not know how to manage it in the best way, statistical analysis becomes fundamental. This process supports the transformation of raw data, converting it into understandable, relevant, valuable and actionable information . Thus, statistical analysis can help create predictive models, identify patterns and trends and assist in informed decision-making in a business.
Check out more details about these benefits!
Supports informed decision making
Statistical data analysis reduces risks and uncertainties in data analysis processes by providing reliable information guided by accurate in-depth analysis. This attribute makes the decision-making process of a company or a specific project much more informed and strategic, optimizing resources and improving results.
Enables modeling and anticipation of trends
Modeling and forecasting are fundamental elements of statistical analysis, benefiting the entire data analysis process by helping to understand and predict complex dynamics. Forecasting helps anticipate future trends , drawing on patterns and historical data to generate insights and guide decisions. Modeling creates analysis models that enable hypotheses to be tested in complex scenarios and systems, helping to create more informed strategies.
Helps identify patterns
Statistical analysis also helps in the process of discovering patterns, relationships and interactions between variables , revealing insights that are often hidden regarding a certain aspect of the business.
By identifying trends, statistical analysis allows us to look beyond the immediate data, identifying patterns and directions that unfold over time.
This characteristic means that statistical data analysis also helps to understand possible relationships and variables between different scenarios and contexts.
Having clarity on these trend patterns and related variables significantly impacts a business's decision-making.
Main types of statistical analysis
Statistical analyses of data are commonly divided into descriptive statistical analysis and inferential statistical analysis .
Check out more details about each of them:
Descriptive analysis
Descriptive statistical analysis aims to pave the way within a data set, functioning as a starting point for deeper analysis later in the process.
It is a way of summarizing the information provided in a simple and direct way, using understandable graphs, tables and visual models .
In this type of analysis, mathematical components such as arithmetic mean, median, mode, standard deviation, regression, range and variance are used to make the data available in a visual and clear way. Therefore, descriptive analysis organizes data sets to support more detailed analyses in the future.
Inferential analysis (modeling)
Inferential analysis is another type of statistical analysis, which, unlike descriptive analysis, has a much deeper bias , going beyond the description and organization of data.
Using data collected in descriptive statistical analysis, this type of analysis aims to test hypotheses and make inferences about the extracted data. These analyses enable the prediction of trends and the creation of data models to assist in business decision-making.
Some key components of this process are:
Estimates : definition of approximate values that serve as parameters of interest to the project or company;
Confidence intervals : is a margin or interval, which offers an estimate of the real value of the established parameter;
Prediction intervals: are sets of values that are intended to make a prediction to identify a specific unknown value in the sample under analysis.
How to apply statistical analysis to projects?
Statistical data analysis can be applied in a variety of contexts, including specific businesses or projects. To do this, it is important to follow a few steps to ensure the process is effective. Check it out:
Determine the objectives of the analysis
Outlining the purpose of statistical analysis is essential and is the first step in this process.
The clarity of this stage, according to the objectives of the project or company, helps to understand what information will be extracted from the analysis, supporting the data collection stage.
Continue reading this article and understand what statistical analysis is, its importance, main types and how to apply them to projects.
Statistical analysis aims to support the process of collecting, analyzing and interpreting data , creating data models capable of raising hypotheses, identifying patterns and trends and making why buy overseas chinese uk directory predictions. This field of data science works with large amounts of data, collecting information from different sources to support the results and their interpretations.
By collecting and cleaning data sets, it is possible to transform them into useful and valuable insights for the business.
This process is based on mathematical methods that, through several components, enable the creation of robust and in-depth data models , capable of extracting insights to support a business's informed decision-making .
Thus, statistical analysis can be applied to different contexts and businesses, such as technology, business, economics, social sciences, medicine, etc.
How important are statistical analyses?
In a context of growing digital transformation, where there is so much availability of raw data from various sources and where companies still do not know how to manage it in the best way, statistical analysis becomes fundamental. This process supports the transformation of raw data, converting it into understandable, relevant, valuable and actionable information . Thus, statistical analysis can help create predictive models, identify patterns and trends and assist in informed decision-making in a business.
Check out more details about these benefits!
Supports informed decision making
Statistical data analysis reduces risks and uncertainties in data analysis processes by providing reliable information guided by accurate in-depth analysis. This attribute makes the decision-making process of a company or a specific project much more informed and strategic, optimizing resources and improving results.
Enables modeling and anticipation of trends
Modeling and forecasting are fundamental elements of statistical analysis, benefiting the entire data analysis process by helping to understand and predict complex dynamics. Forecasting helps anticipate future trends , drawing on patterns and historical data to generate insights and guide decisions. Modeling creates analysis models that enable hypotheses to be tested in complex scenarios and systems, helping to create more informed strategies.
Helps identify patterns
Statistical analysis also helps in the process of discovering patterns, relationships and interactions between variables , revealing insights that are often hidden regarding a certain aspect of the business.
By identifying trends, statistical analysis allows us to look beyond the immediate data, identifying patterns and directions that unfold over time.
This characteristic means that statistical data analysis also helps to understand possible relationships and variables between different scenarios and contexts.
Having clarity on these trend patterns and related variables significantly impacts a business's decision-making.
Main types of statistical analysis
Statistical analyses of data are commonly divided into descriptive statistical analysis and inferential statistical analysis .
Check out more details about each of them:
Descriptive analysis
Descriptive statistical analysis aims to pave the way within a data set, functioning as a starting point for deeper analysis later in the process.
It is a way of summarizing the information provided in a simple and direct way, using understandable graphs, tables and visual models .
In this type of analysis, mathematical components such as arithmetic mean, median, mode, standard deviation, regression, range and variance are used to make the data available in a visual and clear way. Therefore, descriptive analysis organizes data sets to support more detailed analyses in the future.
Inferential analysis (modeling)
Inferential analysis is another type of statistical analysis, which, unlike descriptive analysis, has a much deeper bias , going beyond the description and organization of data.
Using data collected in descriptive statistical analysis, this type of analysis aims to test hypotheses and make inferences about the extracted data. These analyses enable the prediction of trends and the creation of data models to assist in business decision-making.
Some key components of this process are:
Estimates : definition of approximate values that serve as parameters of interest to the project or company;
Confidence intervals : is a margin or interval, which offers an estimate of the real value of the established parameter;
Prediction intervals: are sets of values that are intended to make a prediction to identify a specific unknown value in the sample under analysis.
How to apply statistical analysis to projects?
Statistical data analysis can be applied in a variety of contexts, including specific businesses or projects. To do this, it is important to follow a few steps to ensure the process is effective. Check it out:
Determine the objectives of the analysis
Outlining the purpose of statistical analysis is essential and is the first step in this process.
The clarity of this stage, according to the objectives of the project or company, helps to understand what information will be extracted from the analysis, supporting the data collection stage.