All you need to know about statistical analysis

Statistical analysis is widely used in various fields of life. whether related to business, marketing. scientific research, government business, etc. as it helps us organize, discover, and interpret data. Therefore, statistical analysis is an essential step in the decision-making process and planning for the future.

Statistics, like other sciences, is constantly evolving to keep pace with technological developments and the huge amount of data that needs processing and analysis. In this article, we discuss the definition of statistical analysis, its steps, its most important benefits, types, and different methods. We also discuss the most important statistical analysis programs.

What is statistical analysis?

Statistical analysis is the science of collecting, organizing, exploring, interpreting, presenting, and revealing patterns and trends. Statistical analysis is done in several ways, including: regression equations, ratio analysis, path analysis, and others. The main purpose of these methods is to determine the association between two or more variables and to predict events, and the extent to which they can be achieved again in future events.

Statistics deals with a huge amount of data, and statistical analysis is collecting, classifying, analyzing and interpreting this data and presenting it in its digital form in order to make appropriate decisions. The objectives of statistical analysis include the following:

• Summarize and present data in an easy-to-understand format.
• Find key metrics, including the mean, for a set of data.
• Calculation of spread measures to determine whether the data collected is tight or more spread. and perhaps the standard deviation is the most prominent and widespread example of spread measures.
• Building future forecasts as statistical analysis contributes to decision-making processes related to manufacturing, sports services, banking, retail and others.
• Hypothesis testing where statistics are used in order to test a particular hypothesis where a null hypothesis is disproved or proven.

In general, the goal of statistical analysis is to identify trends in the data subject to analysis, so one of the benefits of statistical analysis is that it makes more data, as it is not an end in itself, but a scientific method.

To clarify more, we will present a quick example of statistical analysis, which is the population census process. First, let us agree that the data in its raw form is useless and does not help us understand reality or plan for the future. In the population census, for example. we usually want to know the number of the population in general, and then count the females. Males, age groups, and others, which we call demographic variables, economic and social status, geographical distribution, etc. Therefore, the population as a whole is not useful, but when we have an accurate classification of the previous categories and the relationship between population increase and some variables and so on. we are in the process of statistical analysis that makes the data more valuable.

Uses of statistical analysis

We do not exaggerate when we say that statistics is used in everything in our lives, as we rely on it in scientific research. industry, commerce, and government institutions. Manufacturers use statistical analysis to improve quality and increase productivity in various fields. including the aviation industry and improving the quality of fabrics.

First: scientific research

Researchers in various scientific fields use statistics to reach results and verify different hypotheses. For example: researchers use statistics to analyze data related to the production of viral vaccines to ensure consistency and safety. It also depends on statistics in social sciences and applied research in varying proportions, which confirms the importance of statistical analysis in the field of Research.

Second: the commercial field

In the field of commerce and business administration, officials in companies use statistics to improve understanding of the market and customer needs. For example: telecommunications companies use statistics to improve their services and network resources, and to reach a clear understanding of the needs of their customers.

Third: government institutions

Different governments rely on statistics, whether in the process of population census or in the economy and various administrative procedures.

Fourth: the commercial field

The stock market goes through great fluctuations, so companies usually use statistics to evaluate a number of their business decisions, including buying and selling shares, in addition to relying on statistical analysis in the process of risk management that the company may be exposed to and assessing the severity of these risks, and finally companies use regression equations To test general hypotheses related to the impact of a number of factors on the company’s assets and the price of its shares on the stock exchange. Statistics are also used in other fields, including:

1. Market research

Where statistical analysis is used in market research. it provides specific numbers about the supply and demand of products, the distribution of customers and their future directions, and others.

2. Business intelligence and data analysis

Statistical analysis is used in the field of business intelligence. as it provides a set of predictions that contribute to the development of future plans.

3. SEO

SEO relies on statistics as the main indicator, as it gives us the number of keywords that the audience is searching for online, which makes us target them.

4. Financial analysis

Technological development has increased the ability to retain and restore various data and is increasing the importance of statistics, as statistical computing becomes more important, including statistical programming and econometrics.

The importance of statistical analysis

Perhaps the previous example explained to us the importance of statistical analysis, as it helps to classify, analyze and interpret data, and therefore provides us with important indicators that are a basic building block in the decision-making process of institutions or governments, in addition to its special importance in the field of scientific research and in various medical, engineering, commercial and industries fields. Various, such as medicine or software and technology companies, and others.

Statistical analysis steps

The statistical analysis is divided into five steps as follows:

• Description: The first step is to describe the data we want to analyze.
• Exploration: The second step is to discover the relationship of the data to the underlying content.
• Create a model: The third step is to create a model to show and understand the correlation of the data with the primary set.
• Model testing: The fourth step is based on testing the model with the aim of proving or disproving it.
• Predictive analytics: The fifth step is to use predictive analytics in order to activate scenarios, which will help in understanding future actions.

The following simplify the steps of statistical analysis in another way:

• Data collection

Statistical analysis begins with collecting data, whether from its primary or secondary sources. For example: we can collect data through surveys, customer relationship management programs, financial reports, online tests, etc., by selecting a representative sample of the original community.

• Data organization

Now we have raw data and we want to organize it or, as statisticians call it, clean it, meaning to remove duplicate data and inconsistencies that may prevent us from obtaining an accurate analysis, and this step is very important because it helps us verify the validity of the data, and thus the validity of the conclusions that we draw from the analysis.

• Display data

After organizing the data comes the stage of presenting the data so that we can analyze it easily. In this step, we use descriptive analysis tools to display the data correctly. Later, we will learn about descriptive statistical analysis and its tools.

• data analysis

The fourth step of statistical analysis is data analysis. In this step, we use statistical techniques to explore relationships and trends. Here, we use deductive and correlational statistical analysis.

• Data interpretation

Now we have conducted the analysis and extracted the various correlations and trends within the data being analyzed. Here comes the interpretation stage after presenting the results in the form of charts, reports, etc.

Types of statistical analysis

Statistical analysis is divided into seven types according to the type of data being analyzed as follows:

1. Descriptive statistical analysis

When we organize and summarize data using numbers and charts, in this case we use descriptive statistics, which mainly aims to facilitate big data and make it interpretable, as it enables us to represent and interpret data efficiently through charts and tables, as it includes a set of operations including tabulation and measurement Central trend (mean, median), measures of dispersion and variance (standard deviation, variance, range) and time series analysis.

We summarize and present the data in the form of tables, charts, and graphs, which contributes to extracting the distinctive characteristics of the data and explaining its basic features, but in this type we do not extract insights about groups that were not observed in the data of the sample being analyzed. Therefore, descriptive statistical analysis is the simplest type of statistical analysis, as it helps to reduce large data and present them in simple forms that facilitate the interpretation process.

Statistics used in descriptive analysis:

• diffusion scale
• measures of central tendency

However, there are a number of disadvantages to descriptive statistics, the most prominent of which is that it does not provide more than quantitative results for the phenomenon, for example when we describe a large group of data using one value as the average, in this case we are between two options, either distorting the original data or losing important information, for example we can calculate Descriptive in business than calculating average revenue, but it doesn’t give us more detail on which products are best sold or which niches are most distributed and so on.

2. Inferential statistical analysis

Inferential statistical analysis allows testing of a particular hypothesis based on a sample of data from which conclusions can be drawn, by applying probability and generalizations about the entire data, as well as the possibility of predicting future outcomes that go beyond the available data.

Deductive or inferential statistical analysis contributes to finding differences between the various groups within the sample, and enables us to test hypotheses.

3. Predictive statistical analysis

When we want to predict certain future events based on a set of facts and figures, whether current or future, we use predictive statistical analysis, by using future technologies and machine learning algorithms to describe the possibility of future outcomes based on real-time data or from the past, data modeling, artificial intelligence, and learning The most prominent automated techniques used in this type of statistical analysis.

Insurance and marketing companies usually rely on predictive statistical analysis, in order to plan for the future and predict future results, such as narrowing the range of risks associated with a future event or achieving competitive advantage gains, etc. Other businesses can benefit from the advantages of predictive statistical analysis for future planning and forecasting. In short, this type of analysis is used Statistician to answer the question what could happen?

4. Descriptive analysis

We use descriptive analysis when we have a large amount of data, and we want to know what is the best action that can happen. It is widely used in business analysis to determine the best possible action in a particular situation. It mainly focuses on discovering the optimal proposal in the decision-making process.

Among the statistical techniques used in this type: simulation, algorithms, graphing, machine learning, and others. In short, descriptive statistical analysis is used to answer the question of what happened. It provides a description of the reality of the phenomenon, and thus helps us make decisions about it.

5. Exploratory data analysis

Exploratory data analysis is the first step in the data analysis process, as it helps in obtaining the main ideas contained within the data. It aims to identify potential relationships, obtain missing data from the data subject to analysis, and examine various hypotheses.

6. Causal analysis

Causal analysis is concerned with providing a clear answer to the question why? It helps us understand the reasons behind the phenomenon and know the things that made the phenomenon appear in this way, for example, this type is used to find out the reason for the failure of a particular project or program to immunize the company from future setbacks, and therefore it is used in:

• Examine the root causes of a problem.
• Understanding what will happen to the variable if one of the other variables of the phenomenon under study changes.

7. Automated analysis

Automated analysis is the least common type among other types of statistical analysis, and it is used to understand how things happen when analyzing huge data, as it is concerned with studying the effect of phenomenon variables on some of them, while excluding intermediate variables or external events that can affect them, as it provides a full explanation to a previous event in the context of the data presented.

Example: If we want to know why startups fail during the early years, and we have a set of data related to startups in a field or in a country, we:

• When we use certain statistical methods to find out the causes of failure, we use causal statistics.
• If we use statistical techniques to describe a phenomenon in its current state and past, we use descriptive statistics.
• If we use statistical techniques to make future predictions and plans, we use predictive statistical analysis.
• We may have a set of data related to startups, so if we want to discover the relationships between variables, such as startups and the marketing process, management, or capital, and extract the relationships between the data provided, then we use exploratory statistics.

There is no conflict between the previous types of statistical analysis, and one or more types of statistical analysis are usually used when conducting specific research, for example: market research uses descriptive and inferential statistical analysis, in order to analyze the results and reach conclusions.

Methods of statistical analysis

There are five common methods of statistical analysis as follows:

First: the average

The mean is the simplest form of statistical analysis. It aims to determine the central point of a data set. It is calculated as follows:

Average = set of numbers ÷ number of items

Example: If we want to extract the average from the following numbers 1,2,3,4,5,6, we will add these numbers together, then divide it by a number of 6 numbers so that the average is 3.5

The average is characterized by being easy to calculate and helps in determining the general trend of the data. As for its defects, it appears when the data subject to analysis contains a large number of outliers or a skewed distribution. In this case, the average does not provide the accuracy that we need to make the decision.

Second: the standard deviation

Standard deviation measures how spread the data is around the mean. A high standard deviation means that the data is widely dispersed from the mean, while a low standard deviation is when most of the data is closer to the mean. One of its drawbacks is that just like the average it can give us inaccurate data.

Example of standard deviation: When conducting an opinion poll or questionnaire form towards a specific service or product, you can analyze the results of the respondents’ answers, and then measure the similarity or difference of the answers. If the similarity is significant, this means that the standard deviation is low and vice versa.

Third: decline

Regression is used to find the relationship between an independent variable and another dependent variable, as it helps in tracking how the variables affect each other, and the regression shows the strength or weakness of the relationship between two variables and how it differs from one period to another.

Fourth: hypothesis testing

We use hypothesis testing when we want to make sure that a conclusion is valid for a specific data set by comparing the data with a certain assumption. There may be a relationship between the variables or not at all, which is known as the null hypothesis. Example: We can use hypothesis testing to find out the relationship between food type and health status or between academic achievement and advanced age, and the null hypothesis could be that there is no relationship between advanced age and academic achievement and so on.

Fifth: Determine the sample size

The indigenous population that we are conducting research on is usually very large and it is difficult to research all of its populations. So we choose a representative sample of the original community and then generalize the results to it. and this is what we call determining the sample size, and in order to do this correctly we need to determine the appropriate size of the sample to be representative of the original community and accurate, and to achieve this we take samples because small samples may not be Expressive, as for large samples, they may be a waste of time, effort, and money. From here stems the importance of samples in statistical analysis. as they are the decisive factor in the veracity and validity of the results or not.

Statistical analysis tools “statistical analysis software”

The computer plays a major role in the development of statistical analysis. as statistical analysis programs have provided huge capabilities to deal with big data and perform complex calculations easily. accurately, and with great speed. and the statistical analyst usually deals with huge data. so analysts use statistical analysis programs such as IBM SPSS and RMP That helps them perform complex analyzes through the additional tools that these programs provide for organizing, interpreting and presenting data sets. The following is a brief explanation of the capabilities of a number of statistical analysis programs:

• IBM SPSS

IBM SPSS is the most famous program among statistical analysis programs, especially in the scientific and academic community, as the program covers a lot of analytical operations from data preparation, analysis and reporting, in addition to the program’s customizable interface, and SPSS is widely used in the field of social sciences.

• R Foundation for Statistical Computing

It is a free program and is widely used in human behavior research. Despite the importance of the program, it requires a high degree of expertise. as it requires a certain degree of coding, which means that it is suitable for experts, not beginners.

• MATLAB (the Mathwirks)

MATLAB is both an analytics platform and a programming language, widely used by engineers and scientists.

• Excel

Excel offers a variety of tools for working with data and statistics, and it’s easy to create graphics, designs, and more. Therefore, Excel is a suitable tool for people who want a basic view of their data, and it is easy to use.

• SAS software

It is a basic system for statistical analysis developed at North Carolina State University, by the SAS Institute for Data Management and Advanced Analytics, where the program enables its users to mine, manage and retrieve data, and SAS is frequently used in business, health care and human behavior research.

• Graphpad Prism

Graphpad Prism can be used in various fields, but it is most relied upon in the field of biological research, as it can perform more complex statistical calculations.

• Minitab software

It is a set of statistical and advanced tools that are used in data analysis, and it can implement the required through text commands or a graphical user interface, thus it is useful for beginners and experts alike.

Choosing any of these programs depends on:

• The experience the user has in coding.
• Search type and find out the stats.

The success of the program in providing correct results that can be generalized to the indigenous community depends on the validity of the data subject to analysis. No matter how advanced the statistical program is. without correct data its results will be useless, no matter how accurate they are. You can hire a statistical analyst on the Fiverr platform for professional statistical analysis services for your company.

Are the results of statistical analysis always true?

Although everyone bets on the results of the statistical analysis process. there are drawbacks to this process, so we must have some prohibitions from the results. because sometimes the statistics are completely wrong. Perhaps the most famous example of this is Simpson’s paradox, which shows that even the best statistics can be unhelpful. University of Berkeley acceptance rates showed that the average acceptance of women was higher than that of men when in reality the exact opposite was true. which means that the results of statistical analysis can be misleading. Occasionally.

In conclusion, in this article, we have covered the definition of statistical analysis. its steps, importance, and various uses. including the use of statistics to interpret research or design surveys and studies. statistical modeling, business intelligence, etc. in addition to the types of statistical analysis and the most important statistical analysis programs. and how to choose between them according to the type of research and the extent of experience The researcher and his knowledge of coding, statistics and features of statistical analysis.