What Is Statistical Analysis? Definition & Key Concepts

Statistical analysis is ‘a computational process conducted by researchers to obtain reliable scientific results for their research through various statistical methods that suit the nature of the data and the target sample.’ Statistical analysis has currently become a primary process in handling the quantitative aspects of economic, social, and other phenomena through appropriate statistical methods and tools. Due to the importance of statistical analysis, we have, through this article, provided an introductory overview of statistical analysis, statistics, and its importance for scientific research, along with the most commonly used methods and illustrative examples of the statistical analysis process as models from our previous work.

Definition of Statistical Analysis:

Linguistically, the term ‘statistics’ means ‘the process of counting or tallying things, derived from the word ‘yuhassiu,’ which means using pebbles or small stones as a primitive means of counting many things.’

As for the technical definition of statistical analysis, it is ‘a process that provides researchers with a set of principles, laws, and various scientific methods for collecting, tabulating, summarizing, presenting, and analyzing data, then drawing conclusions and making the best decisions to solve various problems based on the scientific analysis of available data.’

What Is the Importance of Statistical Analysis in Scientific Research?

The importance of statistical analysis in scientific research lies in its connection to the steps and procedures of scientific research, which include:

1. It helps determine the basic conditions for the objectivity of experiments, their planning, tools, and methodology.
2. It determines appropriate analysis methods for each experiment and the extent of generalization that the results of these experiments entail.
3. It enhances the reliance of research in various sciences on the scientific method, which is based on careful observation, scientific experimentation, mathematical analysis, and logical inference.
4. The use of statistical analysis extends to a large number of different sciences.
5. Statistical analysis provides research and problems that lack complete information with the methods and rules that can be used to make wise decisions that have a positive impact on such studies.
6. Statistical analysis contributes to formulating laws and theories that interpret the results of various research studies.

What Are the Different Methods of Statistical Analysis?

Statistical analysis is considered a fundamental tool in scientific research, which researchers use to understand and comprehend data and obtain reliable scientific results. Statistical analysis has different methods of use depending on the type of collected data and the objectives of the study, the most important of which are:

First: Descriptive Analysis Method:

It aims to summarize and describe data using various statistical measures such as (mean, median, and standard deviation). This method focuses on providing a clear picture of the characteristics of the studied sample without drawing conclusions about relationships or differences.

Second: Inferential Analysis Method:

This type of statistical analysis relies on using samples to generalize results to the larger population. This analysis includes hypothesis testing and using probabilities to estimate population parameters. The most famous methods of inferential analysis are: the ‘t’ test, Analysis of Variance (ANOVA), which is used to compare means between three or more groups to determine if there are statistically significant differences between them, in addition to linear regression.

Third: Regression Analysis Method:

Simply put, regression analysis is ‘a statistical analysis method used to analyze data containing two or more variables when the goal is to discover the nature of this relationship. Regression analysis is one of the most commonly used statistical analysis methods in various sciences because it describes the relationship between variables in the form of an equation.’

Do you have questions about statistical analysis? You can refer to a comprehensive article onStatistical analysis in scientific research.

What Tools Are Needed to Conduct Statistical Analysis?

In order for the researcher to perform statistical analysis, he must have a set of tools, but what the researcher must determine before starting to perform statistical analysis:

First: Choosing the Type of Statistical Test:

Initially, the researcher must conduct a test of the type of statistical test, and this is done according to a set of bases which are:

1. The nature of the specific data that relates and is directly related to the dependent variables.
2. The type of relationships that the researcher wants to choose and perform statistical analysis for.
3. Determining the number of independent variables that the researcher will study.
4. Determining the number of levels of the independent variables.
5. The researcher must have sufficient ability to distinguish between parametric and non-parametric tests.
6. The researcher must test his hypotheses.
7. The researcher must determine the level of statistical significance.

Second: Choosing Statistical Programs:

1. Statistical programs are a set of programs that include important tools for conducting statistical analysis, and they are used with quantitative and qualitative data.
2. The statistical programs provide complete services for researchers and companies, helping them to perform statistical analysis effectively.
3. Statistical programs provide reliable and accurate results for the data that the researcher analyzes.
4. Statistical programs save researchers a lot of time and effort as one of the statistical analysis tools.
5. The most important types of statistical programs are (SPSS), (SAS), (MINITAB), (STATA), and (Excel).

How Can You UseSPSSIn Statistical Analysis?

SPSS is one of the important programs in statistical data analysis, as it is used in many fields including (medical, engineering, agricultural, humanities, social sciences, and others) when performing statistical calculations and illustrative charts, using menu bar commands and dialog boxes, and you can use SPSS in statistical analysis through the following:

1. The program consists of three main windows, represented in the (Data View) screen, which is concerned with displaying data.
2. The (Variable View) screen is the one through which variables are displayed.
3. Finally, the (SPSS Viewer) screen displays the results or outputs, whether they are graphs, tables, or analysis results.

Advantages of Using the ProgramSPSSIn the Statistical Analysis:

1. Very easy to handle and use, suitable for all categories and users whether students, researchers, or experts.
2. Statistical analysis can be performed using lists without needing all the other commands included in the program.
3. The SPSS program can handle all types of files regardless of their extension.
4. The SPSS program is compatible with descriptive analysis style, regression analysis, and other different statistical analysis methods.

Disadvantages of Using the ProgramSPSSIn the Statistical Analysis:

1. The program is paid and not free.
2. It focuses primarily on statistical methods used in social sciences.
3. There is a continuous need to purchase more other packages to handle different purposes.
4. It cannot handle very large data files.

How to Analyze Statistical Data Effectively?:

For a researcher to analyze statistical data effectively, they must follow the following steps:

1. Clearly and accurately define the research problem or objective because if the research objective is unclear, the results will be vague and inaccurate.
2. Determine the tool to be used in collecting data from the sample representing the community (observation, questionnaire, interview).
3. Determine the sample size from which data will be collected, ensuring that the data collection method is compatible with the nature and characteristics of the sample.
4. Code and convert data into numbers or letters to facilitate entering it into the computer and handling it.
5. Perform statistical analyses according to the objectives that the researcher seeks to achieve and the main goal of statistical analysis in scientific research.
6. Before proceeding with data entry and analysis, the researcher should review the basic pillars of statistical analysis represented by (variables, sample selection, questionnaire design), as these pillars largely determine the type and method of statistical analysis.

Research on Statistical Data Analysis:

There are many scientific studies that have addressed statistical analysis in scientific research, and we mention among them the research of Jamal Ahmed Al-Shuadafi under the title “Ready-made Programs and Statistical Data Analysis” published in the Journal of the Salim Abdullah Kamel Center for Islamic Economics, which includes:

1. A review of the most important ready-made statistical packages that can be used in statistical data analysis.
2. Clarification of the most important advantages and disadvantages of each statistical program and their websites on the Internet.
3. A review of the possibility of analyzing data statistically remotely via the Internet, and the most important websites and programs that support this.
4. The researcher discussed the most important statistical analysis programs in scientific research such as (SPSS, SAS, MINITAB, BMDP, S-PLUS) and other programs.

Examples of Statistical Analysis:

Among the examples of statistical analysis methodology, we focus on the initial stages of statistical analysis represented by determining the statistical population, statistical unit, statistical variable and its type based on the following examples:

1. Lifespan of electric bulbs produced in a factory.
2. Classification of cars at an agency by color.
3. A statistical study on the number of rooms in housing units for a sample of 100 houses in Tiaret state.
4. Distribution of a sample of 50 workers according to their monthly wages in a company.

The Solution:

You can view practical examples of conducting a complete statistical analysis from our previous work while preserving the intellectual property rights of their ownersOur previous work in statistical analysis.

Related Articles:

1. Inferential statistics in scientific research
2. Descriptive statistics in scientific research