Statistical Analysis: Definition, Types, Importance & More

Statistics is another technique of studying data to ascertain relative trends, relationships and patterns. They use and believe in it, and their belief implies that it has valuable solutions for researchers, governments, businesses, and organisations to generate sensible results from the numbers. Therefore, statistical analysis requires adequate planning to make dependable recommendations. This refers to the choice of objectives, the formulation of your study questions and the choice of sample and sampling techniques.

After collecting data, one may aggregate and categorise the data with simple statistics, then use more advanced statistics to forecast the results on the entire population sample. Finally, the objective is to analyse and report the results to others. This guide introduces statistical analysis for students and researchers, showing each step through two examples: one is used to establish whether there is a direct correlation between two events and the other is used to determine the relationship between two factors.

Statistical Analysis Definition

The term statistical analysis refers to the collection and study of data to uncover patterns and trends. Using numbers also helps to remove bias, making data easier to understand. This approach can be applied to help us understand research results, create models, and plan studies or surveys.

Statistical analysis is important in AI and machine learning because we use large data sets. We can organise them by common patterns and turn data into valuable information. In short, statistical analysis is a way to turn raw, unorganised data into something useful.

Statistical analysis can help businesses and organisations predict future trends using past data. It is the science of dealing with data to try to draw conclusions that can be read, used to inform, or used to help develop a plan.

A set of steps in statistical analysis transforms raw data to create meaningful insight. Here’s a breakdown of the key steps:

• Define the Data: First, you need to know what kind and how the data is. This includes data sources, especially the types of variables and formats of collecting data that can contribute to true analysis.
• Relate Data to the Population: Compare your sample data to the population it relates to. This step enables us to determine whether our sample truly reflects the entire population for which we are conducting this study, which is very important when we want to draw valid conclusions.
• Build a Model: Statistical modelling attempts to create a statistical model that describes the relationship between different variables in your data. This model removes the complexity from the data and simplifies it so that the patterns and trends of interest to the population are easier to see.
• Validate the Model: Check how your model explains the data—check its accuracy. This step is often hypothesis testing, which checks the model assumptions to ensure the results are trustworthy and valid.
• Use Predictive Analytics: Finally, use predictive method to predict future outcomes. This step runs scenarios that help organisations and researchers make informed decisions and develop action plans responsive to anticipated future trends.

Also Read: Statistics for Data Science

Types of Statistical Analysis

There are several variations of statistical analysis, each aimed at doing different things and doing them differently. Here are the six main types:

Descriptive Analysis

The descriptive analysis collects, interprets, and summarises data in an easily understandable way. It delivers data in charts, graphs, and tables without concluding anything.

Inferential Analysis

Inferential analysis involves making predictions or inferences from a sample of data to infer what it might be like in a larger population. It finds relationships with variables and makes it possible to generalise findings from a smaller sample to a larger group.

Predictive Analysis

Predictive analysis uses past data to predict the future. It uses tools like machine learning, data mining, and statistical modelling to spot and predict a trend using historical data.

Prescriptive Analysis

Prescriptive analysis goes even further by recommending actions based on data insights. This type of analysis allows for making informed decisions based on suggestions of the best course of action that will naturally arise and result from the analysis.

Exploratory Data Analysis

Here, exploratory data analysis considers potential relationships in data with no expectations. It looks for unknown patterns and connections, which might help you see insights that might not seem obvious.

Causal Analysis

Causal analysis tries to find relationships between cause and effect. It helps explain why things happen and how they affect others to understand why outcomes occur, like in a business performance analysis.

Importance of Statistical Analysis

Statistics is a simple way to simplify complex data to make informed decisions in many fields. Here are some key reasons why statistical analysis is so valuable:

• Simplifies Large Data Sets: Statistical analysis organises huge quantities of data into easily understood and manageable pieces so you can make more sense of an overwhelming amount of information (and derive more meaning from it).
• Supports Research and Experiment Design: Statistical methods are required to plan effective laboratory, field, and survey studies and obtain accurate, reliable results.
• Enables Strategic Planning: Developing well-founded plans for any field of study based on statistical analysis is very helpful to research and decision-making.
• Helps Make Predictions and Generalizations: However, researchers have used statistical techniques to forecast outcomes and identify trends in diverse fields, from economics to climate science.
• Broad Application Across Fields: Statistical methods are used in almost every field, from physical and biological sciences (like genetics) to business, social sciences, and public health.
• Used by Various Professionals: Statistical analysis is used by business managers, researchers, and manufacturers who base their decisions on the information they obtain to improve their operations.
• Assists Government and Business Administration: Administrators use statistics to create and modify policies, use data trends to develop and implement policies, allocate resources, and judge program effectiveness.
• Supports Political and Social Insights: Politicians and policymakers use statistics to argue points, assuage public fears, and determine policy effectiveness.

Common Software for Statistical Analysis

Often, statistical analysis involves working with large datasets — that would be too tough to handle by hand. Luckily, there is software dedicated to making big data analysis efficient and accurate. Learning these tools will help you improve, making you a desirable employee and good for working on tough projects.

In descriptive and inferential analysis, statistical software is important. It can visualise (create charts and graphs) or do complex calculations to find reasonable conclusions from data. While the software you use may vary by job, here are some of the most commonly used tools in statistical analysis:

• SAS: SAS is widely appreciated for its capability to handle and analyse data, perform precise predictive analytics steps, and produce other detailed statistics.
• R: R is arguably one of the most recommended open-source technologies for computing, graphical, and statistical data analysis. It is widely used in research and academia.
• SPSS: SPSS is frequently used in the social sciences as a basic application for analysing surveys and visualising the results.
• Minitab: Minitab is commonly used in quality improvement projects. It offers user-friendly methods of statistical analysis for manufacturing and business.
• Stata: Stata has been publicly used in economics, biostatistics, and political sciences. It operates on elaborate data formats and provides statistical information.

Statistical Analysis Methods

Many methods used in statistical analysis play their roles in interpreting the data. Here are five of the most commonly used techniques:

1.     Mean

A straightforward way to find a dataset’s central tendency is with the mean or average. The trend is an average of all the data points added up and then divided by the number of data points, the mean. While I use the mean, sometimes it may be inaccurate if the data has outliers.

2.     Standard Deviation

It tells you how spread out your data points are from the mean. The advantage is knowing whether results can be generalised and within data variability. The smaller the standard deviation, the closer the standard deviation data are to the mean, and the larger the standard deviation, the greater the variability.

3.     Regression

Regression analysis tries to find the relationship between variables (or the cause-and-effect relationship between an independent and dependent variable). This is why it is so commonly used: to predict future events given existing trends.

4.     Hypothesis Testing

Hypothesis testing uses sample data to determine whether a hypothesis or assumption is valid. With an initial assumption, researchers start with the data and try to confirm or refute it, adding rigour to research conclusions.

5.     Sample Size Determination

Sample size determination or sampling involves selecting a subset of an entire population to represent it. It’s especially useful for analysing big data. Research objectives determine which common sampling techniques will be used, such as random, convenience, or snowball sampling.

Statistical Analysis Example

To demonstrate statistical analysis, let’s calculate the standard deviation of a set of test scores.

Suppose we have five test scores:

• Calculate the Mean:

Mean = (15 + 10 + 8 + 12 + 5) / 5 = 50 / 5 = 10

• Calculate the Average of the Squared Differences:

Sum of squares = (25 + 0 + 4 + 4 + 25) / 5 = 58 / 5 = 11.6

• Find the Sample Variance:

Sample Variance = 11.6

• Calculate the Standard Deviation:

Standard Deviation = √11.6 ≈ 3.41

In this example, the standard deviation of 3.41 shows the average spread of test scores around the mean score of 10. This measure helps in understanding the variability of scores.

Career in Statistical Analysis

There are lots of options available in industries for a career in statistical analysis. On average, a Statistical Analyst has a solid familiarity with statistics, data analytics, or some associated areas like maths or Computer Science.

As Data Analysts, they enter and work with data to discover trends and patterns used to make decisions. If you’re aiming for more senior jobs such as Data Scientist, being able to code in languages such as Python or R and learning data visualisation tools (like Tableau) are essential. As you move on, creating predictive models and using machine learning techniques have become advanced statistical analysis skills.

Senior Data Analyst and Data Scientist roles with this kind of career growth require leadership roles on projects or teams, strategy development, and vast amounts of complex analyses. As skill grows, we would choose to hang out with just one or two areas: financial analytics, health data, and consumer behaviour, all with their own challenges and payoffs.

No matter if you are starting up your Data Analyst journey or hope to hold leadership positions in the future, mastering the usage of various statistical tools and knowing the distinct data are fundamental to a career in statistical analysis.

Conclusion

Statistical analysis is a powerful thing that gives raw data a meaningful shape, allowing individuals and organisations to make informed decisions. Statistical analysis uses many methods and software to discover trends, set relationships, and give a basis for trustworthy predictions. It is essential to analyse data with accuracy and insight, whether in business, research, social sciences or government. Because we have more data than ever, it is becoming critical for us to master statistical analysis as we gather and use more data than ever on the world around us to make progress in the ever-growing field. For further assistance, refer to the Certificate Program in Financial Analysis, Valuation, & Risk Management powered by Hero Vired with edX and Columbia University.