The research study will be on the impact of medical and non-medical interventions on childhood obesity. The data collected will be analyzed through Microsoft Excel as it is easy to use and it can handle large statistics. In this study, correlation and ANOVA will be the major types of evaluation which will be conducted. At the end of the study, the hypothesis will be tested. Finally, based on the outcomes, the researcher will either accept or reject the null hypothesis. Variables Variables are the most essential in the study as they outline what will be measured at the end. The research on the impact of medical and non-medical interventions on childhood obesity will have two significant variables: the number of patients and the nature of medical interventions applied. The latter is the independent variable, while the former is the dependent one. The researcher will apply the variables during data collection and further in the analysis of the study. Null Hypothesis In this research, both null and alternative hypotheses play an essential role in the outcome of the analysis. The section is vital as it brings more understanding of the study question’s metrics (Willmott, 2020). Therefore, in this research, the null hypothesis is that the nature of intervention affects obesity disorders. On the other hand, the alternative hypothesis is that non-medical interventions have an impact on obesity disorders. As per the ANOVA and correlation outcome, the researcher can either accept or reject any of the hypothesis forms. Therefore, the hypothesis is the most critical section of this study. The Type of Analysis Data analysis is the stem of research; it finalizes the outcome as per the data collected. In this research, ANOVA and correlation analysis will be computed to test the hypothesis and significance level. Through ANOVA, the level of significance will be applied to either reject or accept the null hypothesis. Similarly, correlation analysis will be the primary factor used in testing the relationship between the independent and dependent variables. Therefore, it will be easy for the researcher to develop a conclusion by finding the tests conducted. ANOVA will be used because it recognizes the characteristics of both independent and dependent parameters. The difference between the variables of the study will determine if they are related. Comparably, the correlation will be applied as it gives the critical relationship between the quantities measured in the study. Thus, by comparing the answers which are computed, it will be easy to give recommendations for the future. The Level of Significance In this research, a significance of 5%, which is 0.05, will be applied. The level is important in hypothesis testing. More significantly, in the decision to accept or reject the null or alternative hypothesis, the actual values are compared to the 5% figure. There is minimal risk at a 5% significance level, which implies a higher confidence interval in the research. Therefore, it will be easy to eliminate any bias level during the study with 95% confidence. Nature of the Results In this study, the researcher is looking forth to the impact of medical and non-medical interventions on childhood obesity. Research by Lombardi et al. (2020) has affirmed that the nature of the intervention plays a major rol