How would you determine if the assumptions of a statistical model have been met?

In my final year of college, I worked on a research project that involved analyzing survey data to understand student satisfaction. To ensure that our statistical model was valid, we first checked the assumptions of normality and homoscedasticity. We used visual methods like Q-Q plots and scatter plots of residuals to assess these assumptions. By identifying any deviations early on, we could adjust our approach, such as transforming variables or opting for non-parametric tests. This hands-on experience taught me the importance of these assumptions in delivering reliable results and the insights they provided for our conclusions.

During my time volunteering for a local non-profit, I was tasked with analyzing the impact of our community programs on local youth engagement. I started by collecting data and then performed various statistical analyses. To check if the assumptions of our chosen models were met, I created histograms and box plots to evaluate normality and looked at residual patterns to assess homoscedasticity. This experience helped me understand how critical it is to validate assumptions before drawing conclusions, as it ensured our findings were credible and could effectively influence future program decisions.

In my first job as a junior data analyst, I worked on a project involving client sales data. One of my responsibilities was to prepare reports based on regression models. I routinely checked the model assumptions by performing residual analysis. I noticed that some models displayed patterns in the residuals, indicating violations of assumptions. I presented these findings to my team, which led us to explore alternative models. This experience reinforced my belief in the importance of thoroughly validating assumptions to ensure that our analyses provided actionable insights for our clients.