A Comprehensive Review of Statistical Analysis Techniques in Economic Data Studies

Abstract

Statistical analysis plays a key role in the field of economics, enabling scientists to extract meaningful information from large and complex data sets. It is essential for forming policies, comprehending market movements, and forecasting future economic scenarios. Choosing the right statistical analysis tools is crucial for gaining useful insights from economic data in this era of expanding data availability. Robust statistical analysis methods further increase the credibility and reliability of economic research by addressing complexity, removing data limitations and reducing bias, ultimately leading to more accurate and meaningful conclusions. With an emphasis on the value of exploratory data analysis, hypothesis testing, and regression analysis, the study begins by elucidating the fundamental ideas of statistical analysis in the context of economic data. The subject matter then moves on to descriptive statistics, focusing the importance of statistics like mean, median, and standard deviation in capturing the main trends and outliers in economic information. The conversation then shifts to inferential statistics, illustrating how statistical tests make it easier to infer significant results from sample data, which may subsequently be used to shed light on broader economic trends. The aim of this review article is to provide a comprehensive overview of the different statistical analysis techniques commonly used in the study of economic data. We examine the applications of these techniques in different economic contexts and highlight their strengths, limitations and potential challenges. This article also discusses the importance of robust statistical methods for making informed policy decisions and promoting evidence-based economic research.