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Statistics is a branch of mathematics that focuses on observing, organizing, and analyzing large amounts of data. It is deeply embedded in various aspects of our daily lives. While Big Data analysis has recently gained prominence, statistics have been traditionally used in numerous industries and fields long before the advent of the data era. From economics, psychology, education, marketing, politics, and agriculture to engineering, the importance of statistics is being increasingly recognized, even in high school research projects.



The Importance of Statistics in Research
 

When Did the Data Era Begin?

Compared to other fields, statistics might seem relatively young. However, scholars trace the origins of statistics back to the mid-17th century, with it being formally recognized as a discipline in the 20th century. By the early 19th century in Europe and the United States, data began to capture significant attention, leading to what is known as the "Age of Statistical Enthusiasm." Although the statistics of that time did not encompass modern data analysis methods and theories, they provided valuable scientific insights into societal changes. For instance, following the French Revolution of 1789, statistics were used to highlight issues like crime, suicide, poverty, and public health, reflecting society's darker aspects. Today, with the advent of the Fourth Industrial Revolution and the era of Big Data, the scope and application of statistics have expanded dramatically.

 

Statistical Inference vs. Descriptive Statistics

Statistical Inference involves using sample data to make generalizations about a larger population. It often includes creating graphs and tables for analysis, focusing on large-scale groups for greater efficiency. For example, instead of measuring the diameter of every nail produced in a factory, a representative sample of nails is measured to generalize about the diameters of all nails produced.
 

Descriptive Statistics involves summarizing, presenting, and interpreting collected data to highlight its characteristics. It uses various methods to analyze relationships between factors, determine which samples belong to which populations, and identify significant factors within a population. Unlike statistical inference, descriptive statistics emphasize summarizing data into single metrics or visualizing data.

 

What is R?

R is a software and interpreted programming language designed for statistical analysis and graphics. Developed in 1995 by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, the language is named after the initials of their first names.
 

   - Rich Statistical Packages: R offers numerous packages for basic statistics and visualization.

   - Intuitive and Varied Graphical Representations: R excels in representing complex data in intuitive and diverse graphical forms.

   - Data Mining, Big Data Processing, and Machine Learning: R is highly useful in these advanced fields.

   - Free and Open-Source: R is accessible to everyone without cost.

The Importance of Statistics and Big Data in Research

Creating a quality research project requires background knowledge, observation of phenomena, hypothesis formation, data collection through experiments or research, data analysis, and explaining research results. Statistics play a crucial role throughout this process. Managing and analyzing exponentially growing data requires statistical skills. Additionally, obtaining statistical data to support claims is essential for drawing meaningful conclusions. Therefore, statistics are vital in transforming data into valuable information and are indispensable in research.

   - Improved Reliability: Analyzing large datasets helps develop more accurate prediction models, enhancing the reliability of research by distinguishing between random results and actual effects.

​   - Increased Efficiency: Statistics assist in all stages of experimentation, from setting variables and sample sizes to preventing potential obstacles, thereby increasing efficiency.

   - Facilitating Comparisons: Statistics allow for easy comparison of one's research results with others, helping to evaluate and contextualize findings.

   - Easier Interpretation: Visual representations of statistical data make it easier to interpret research results and assess the generalizability of data.

   - Real-Time Monitoring: Statistics enable real-time monitoring, allowing for quick and precise decisions, improving data collection, and understanding research conditions swiftly.

 

Evaluation Methods of International Journals

Quality international research journals prioritize detail and principles in their evaluations, distinct from general articles. Criteria such as large sample sizes, high complexity, and significance are crucial and achieved through meticulous statistical analysis. Notably, journals like SCI often feature logistic regression, Kaplan-Meier survival analysis, and chi-square tests. Especially in fields like medicine, where sample sizes may be limited, choosing research topics with careful statistical consideration is essential.