# What is Statistical Analysis? Types and Examples - BPI - The destination for everything process related (2023)

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In this blog, we will get to know what is statistical analysis real meaning, why statistical analysis is necessary, types of statistical analysis, and many more interesting subtopics related to statistical analysis.

Here are the following topics we are going to discuss:

• What is Statistical Analysis?
• Why do we need Statistical Analysis?
• Types of Statistical Analysis
• Statistical Data Analysis Methods
• Benefits of Statistical Analysis
• Conclusion

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## What is Statistical Analysis?

The mathematical field of statistical analysis deals with the collection, organization, analysis, interpretation, and presentation of data. It is a technique for extracting information from vast volumes of data and deriving important conclusions from it.

Statistical Analysis is used in a wide range of fields, from research and development to marketing and Data Science. It is used to identify trends in customer behavior, test the effectiveness of new products, and measure the success of marketing campaigns.

Statistical Analysis is also used to identify areas of improvement in operations, and to identify customer segmentation and target markets.

Effective statistical analysis requires a firm understanding of the underlying data as well as a detailed understanding of statistical methodologies and procedures.

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Finding patterns, trends, and correlations in data sets is the aim of statistical analysis. This is accomplished through the use of numerous statistical techniques and methodologies, including regression analysis, hypothesis testing, and descriptive statistics.

These results can be utilized to anticipate the future, make wise judgments, and comprehend the fundamental causes of observable occurrences.

Statistical Analysis is an important tool for businesses of all sizes. It provides a powerful way to make sense of large sets of data, helping organizations make better decisions, increase efficiency, and maximize profits.

## Why do we need Statistical Analysis?

In order to comprehend and interpret the data we gather, statistical analysis is a crucial tool. The significance of statistical analysis is best exemplified by the following points:

Identify patterns and trends: Data that may not be obvious to the human eye can be analyzed statistically to reveal patterns and trends. It aids in our comprehension of the relationships between variables and how they evolve through time.

Make data-driven decisions: We can make informed decisions due to statistical analysis, which gives us new perspectives. It aids in assessing the importance of the findings and their applicability to a wider population.

Evaluate hypotheses: By statistical analysis, we may assess hypotheses by comparing them to the data. It enables us to assess the likelihood that the hypothesis is correct or incorrect.

Improve processes: Statistical analysis enables us to pinpoint process improvement opportunities. Data analysis allows us to spot bottlenecks and inefficiencies and implement improvements that boost output and efficiency.

Predict future outcomes: Looking at historical trends and patterns, statistical analysis enables us to forecast future results. It lets us to determine the possibility that specific occurrences will occur and then base our judgments on that knowledge.

## Types of Statistical Analysis

There are several different types of statistical analysis, each of which has its own strengths and weaknesses. Some of the most common types of statistical analysis examples include:

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• Descriptive Statistics: The primary objectives of this kind of analysis are to summarize and describe a dataset’s fundamental characteristics, such as its mean, median, and standard deviation.
• Inferential Statistics: The kind of statistical analysis used to derive inferences from data is known as inferential statistics. It entails using sample data to infer relevant information about the population the sample was taken from. It uses methods including correlation analysis, regression analysis, and hypothesis testing.
• Predictive Analysis: A sort of statistical study called predictive analytics is used to pinpoint prospective outcomes or trends. It entails the use of data mining tools to find patterns in data and the subsequent use of those patterns to forecast the future.
• Regression Analysis: Analysis that models the link between two or more variables is known as regression. Regression analysis is widely used in predictive modeling to show how a dependent variable is related to one or more independent variables.
• Multivariate Statistical Analysis: In this type of Analysis, multiple variables can be analyzed concurrently using multivariate statistical analysis. There are various techniques that are included in a multivariate analysis that are factor analysis, cluster analysis, and discriminant analysis.

## Statistical Data Analysis Methods

Statistical data analysis methods are a crucial tool for making informed decisions based on data. There are several commonly used statistical data analysis methods, including:

• Mean

Mean is a commonly used measure of central tendency in statistical data analysis. It represents the average value of a set of data and is calculated by summing all the values in the dataset and dividing by the number of observations.

The mean is a useful measure when the data is normally distributed and the outliers do not significantly affect the overall pattern of the data.

• Regression

Regression Analysis is a statistical method used to model the relationship between two or more variables. It is a powerful tool for predicting the value of a dependent variable based on one or more independent variables.

Regression analysis can be used to examine the strength and direction of the relationship between variables, and to make predictions about the value of the dependent variable based on a given set of values for the independent variables.

• Hypothesis Testing

The statistical method of hypothesis testing enables researchers to infer information about a population from a sample of data.

It includes formulating a hypothesis, gathering data, then analyzing that data to determine whether or not the theory is justified.

A lot of industries, including market research, clinical trials, and scientific research, use hypothesis testing.

• Sample Size Determination

Sample size determination is a critical step in statistical analysis that determines the number of observations needed to accurately represent a population.

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The sample size should be large enough to provide sufficient power to detect meaningful differences or relationships while being small enough to limit the cost of collecting data and the potential for false positive results.

## Benefits of Statistical Analysis

Statistical analysis is a powerful tool used in many fields to extract meaningful insights and make informed decisions. Some of the benefits of using statistical analysis include:

• Improved Decision-Making: By analyzing data and identifying patterns and relationships, statistical analysis helps decision-makers make more informed decisions based on empirical evidence.
• Increased Accuracy: Statistical methods help to reduce the impact of random errors and provide more accurate results.
• Better Understanding of Data: Statistical analysis allows you to explore data in-depth, identify trends and patterns, and uncover relationships between variables.
• More Efficient Resource Allocation: By understanding the underlying patterns in data, organizations can optimize resource allocation, resulting in more effective and efficient use of resources.
• Better Communication of Results: Statistical analysis provides a clear and concise way to present results and findings, making it easier for others to understand and utilize the information.

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## Conclusion

Overall, Statistical Analysis provides a systematic and scientifically rigorous approach to analyzing data, allowing individuals and organizations to make better decisions, allocate resources more efficiently, and achieve their goals.

If you have any queries related to this domain, then you can reach out to us at Intellipaat’s Data Science Community!

The post What is Statistical Analysis? Types and Examples appeared first on Intellipaat Blog.

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## FAQs

### What is statistical analysis explain with an example? ›

Statistical analysis is the science of collecting data and uncovering patterns and trends. It's really just another way of saying “statistics.” After collecting data you can analyze it to: Summarize the data. For example, make a pie chart.

What are the types of statistical analysis? ›

There are two main types of statistical analysis: Descriptive statistics explains and visualizes the data you have, while inferential statistics extrapolates the data you have onto a larger population.

What are the 5 basic methods of statistical analysis? ›

The five basic methods are mean, standard deviation, regression, hypothesis testing, and sample size determination.

What are four statistical analysis techniques used to describe the sample? ›

It all comes down to using the right methods for statistical analysis, which is how we process and collect samples of data to uncover patterns and trends. For this analysis, there are five to choose from: mean, standard deviation, regression, hypothesis testing, and sample size determination.

What are the types of statistics and examples? ›

In Maths, Statistics is a method of interpreting, analysing and summarising the data. Hence, the types of statistics are categorised based on these features: Descriptive and inferential statistics. Based on the representation of data such as using pie charts, bar graphs, or tables, we analyse and interpret it.

What is meant by statistical analysis? ›

What is statistical analysis? It's the science of collecting, exploring and presenting large amounts of data to discover underlying patterns and trends. Statistics are applied every day – in research, industry and government – to become more scientific about decisions that need to be made.

What is statistical analysis called? ›

Predictive Analysis

Predictive statistical analysis is a type of statistical analysis that analyzes data to derive past trends and predict future events on the basis of them. It uses machine learning algorithms, data mining, data modelling, and artificial intelligence to conduct the statistical analysis of data.

What is the purpose of statistical analysis? ›

The goal of statistical analysis is to identify trends. A retail business, for example, might use statistical analysis to find patterns in unstructured and semi-structured customer data that can be used to create a more positive customer experience and increase sales.

What is the most commonly used statistical method for analyzing data? ›

Two main statistical methods are used in data analysis: descriptive statistics, which summarizes data using indexes such as mean and median and another is inferential statistics, which draw conclusions from data using statistical tests such as student's t-test.

What are the three stages of statistical analysis? ›

The following are the main stages of statistics: Collection data: Gathering the statistical facts by different methods. Organization of data: The gathered data is organised in the form of tables, graphs, etc. Presentation of data: The organised data is presented.

### What are the 4 statistical process? ›

Consider the use of statistics as a data process, or rather, four processes: measurement, collection, analysis, and interpretation.

What are the elements of statistical analysis? ›

The five elements of an inferential statistical analysis are the population size, number of variables, sample set, satistical inference about the population, and the measure of reliability.

What are the names of the two main types of analysis? ›

Descriptive and inferential are the two general types of statistical analyses in quantitative research.

What is an example of a statistical technique? ›

General Statistics

For example, statistical techniques such as extreme values, mean, median, standard deviations, interquartile ranges, and distance formulas are useful in exploring, summarizing, and visualizing data. These techniques, though relatively simple, are a good starting point for exploratory data analysis.

What are 3 examples of statistics? ›

8 Examples of How Statistics is Used in Real Life
• Example 1: Weather Forecasting.
• Example 2: Sales Tracking.
• Example 3: Health Insurance.
• Example 4: Traffic.
• Example 5: Investing.
• Example 6: Medical Studies.
• Example 7: Manufacturing.
• Example 8: Urban Planning.
Jan 3, 2022

What is a good example of statistics? ›

An example of statistical analysis is when we have to determine the number of people in a town who watch TV out of the total population in the town. The small group of people is called the sample here, which is taken from the population.

What are some examples of statistical questions? ›

A statistical question is a question that can be answered by collecting data that vary. For example, “How old am I?” is not a statistical question, but “How old are the students in my school?” is a statistical question.

What is the easiest 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.

Why is it important to run statistical analysis on the data? ›

Using appropriate statistics, you will be able to make sense of the large amount of data you have collected so that you can tell your research story coherently and with justification. Put concisely, statis- tics fills the crucial gap between information and knowledge.

What are the benefits of statistical analysis? ›

The statistical analysis brings in numerous benefits to make the best usage of the vast data available, such as assisting in market research, product development, mapping out the company's growth rate, improve the efficiency of the company, etc.

### What is the best type of data analysis? ›

Prescriptive Analysis

The final type of data analysis is the most sought after, but few organizations are truly equipped to perform it. Prescriptive analysis is the frontier of data analysis, combining the insight from all previous analyses to determine the course of action to take in a current problem or decision.

How do you summarize a statistical analysis? ›

The three common ways of looking at the center are average (also called mean), mode and median. All three summarize a distribution of the data by describing the typical value of a variable (average), the most frequently repeated number (mode), or the number in the middle of all the other numbers in a data set (median).

How many types of statistical data are there? ›

Types of Data in Statistics (4 Types - Nominal, Ordinal, Discrete, Continuous)

What are the 7 steps in the statistical process in order? ›

1.2: The 7-Step Process of Statistical Hypothesis Testing
• Step 1: State the Null Hypothesis. Note.
• Step 2: State the Alternative Hypothesis.
• Step 3: Set α
• Step 4: Collect Data.
• Step 5: Calculate a test statistic.
• Step 6: Construct Acceptance / Rejection regions.
• Step 7: Based on steps 5 and 6, draw a conclusion about H0. Note.
Jan 2, 2023

What is data analysis and how many types of data analysis? ›

Summary: Data analysis means a process of cleaning, transforming and modeling data to discover useful information for business decision-making. Types of Data Analysis are Text, Statistical, Diagnostic, Predictive, Prescriptive Analysis.

What are the different types of research analysis with example? ›

The different types of data analysis include descriptive, exploratory, inferential, predictive, causal and mechanistic.
...
• Descriptive Analysis. The goal of descriptive analysis is to describe or summarize a set of data. ...
• Exploratory Analysis (EDA) ...
• Inferential Analysis. ...
• Predictive Analysis. ...
• Causal Analysis. ...
• Mechanistic Analysis.
Nov 15, 2022

What is an example of analysis in research? ›

For example, researchers conducting research and data analysis for studying the concept of 'diabetes' amongst respondents might analyze the context of when and how the respondent has used or referred to the word 'diabetes.'

How do you explain statistical analysis? ›

Statistical Analysis Defined

It's the science of collecting, exploring and presenting large amounts of data to discover underlying patterns and trends. Statistics are applied every day – in research, industry and government – to become more scientific about decisions that need to be made.

What is a simple example of statistics? ›

A statistic is a number that represents a property of the sample. For example, if we consider one math class to be a sample of the population of all math classes, then the average number of points earned by students in that one math class at the end of the term is an example of a statistic.

What kind of statistical analysis should I use? ›

If distribution of the data is not normal or if one is not sure about the distribution, it is safer to use non-parametric tests. When comparing more than two sets of numerical data, a multiple group comparison test such as one-way analysis of variance (ANOVA) or Kruskal-Wallis test should be used first.

### What are the 5 types of statistics? ›

There are two kinds of Statistics, which are descriptive Statistics and inferential Statistics. In descriptive Statistics, the Data or Collection Data are described in a summarized way, whereas in inferential Statistics, we make use of it in order to explain the descriptive kind. Both of them are used on a large scale.

What are the 5 main statistics? ›

A summary consists of five values: the most extreme values in the data set (the maximum and minimum values), the lower and upper quartiles, and the median. These values are presented together and ordered from lowest to highest: minimum value, lower quartile (Q1), median value (Q2), upper quartile (Q3), maximum value.

What is the most important reason for doing statistical analysis of data? ›

Statistical knowledge helps you use the proper methods to collect the data, employ the correct analyses, and effectively present the results. Statistics is a crucial process behind how we make discoveries in science, make decisions based on data, and make predictions.

What are two examples statistics? ›

Examples of Statistics
• Example 1:Compute the mean deviation about mean from the following data. Size(x) ...
• Example 2: The mean of 5 observations is 4.4 and their variance is 8.24. If 3 of the observations are 1, 2, and 6, find the other two observations. ...
• Example 3: Find the standard deviation of 8,10,12,14,16.

What is statistics in your own words? ›

Statistics is the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data.

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