Qualitative and quantitative analysis are two basic methods in research and data analysis that are often used in different scientific disciplines. These two approaches require different methods in data collection and they have different goals. They allow researchers to gain comprehensive insights into complex phenomena. For example, a company can use the findings to better understand a target group with its needs, or one can evaluate their effectiveness. Thus, both qualitative analysis and quantitative analysis are important skills in business consulting.In this text, the importance and application of these methods will be highlighted. Since significance and representativeness, as well as the difference between causality and correlation, play an important role in assessing the quality and significance of research results, these terms will first be explained. Explantation of TermsIn statistics, the term "significance" refers to the importance or relevance of observations or differences in the data. Significance indicates whether the observed results in a study are likely due to true effects, or simply represent random variation. If a result is significant, it means that there is a low probability that it was caused by pure luck or chance.Example: Suppose a study is conducted to see if a new drug treatment improves the cure rate of a particular disease compared to the conventional treatment. After analyzing the data, it is found that the group receiving the new drug had a significantly higher cure rate than the group receiving the conventional treatment. This means that the differences in cure rates are not due to chance or luck, but are indicative of the true effect of the new drug.=> If you want a Case Study about the medical field, try out this one: