How do you determine if a dataset follows a normal distribution?

In my statistics class, I worked on a project where we analyzed the test scores of our cohort. To determine if the scores followed a normal distribution, I first created a histogram. I noticed a bell-shaped curve, which initially suggested normality. Next, I used a Q-Q plot, where the points approximately followed the diagonal line, reinforcing my observation. Finally, I conducted the Shapiro-Wilk test, which returned a p-value greater than 0.05, indicating that the test scores did not significantly deviate from a normal distribution. This project helped me understand how visualizations and statistical tests can work together to assess data characteristics.

While volunteering at a local nonprofit, I helped analyze survey responses about community services. I wanted to check if the ratings given by participants were normally distributed. I started by plotting the data in a histogram, which showed a slight skew, but I wasn’t sure. I then created a box plot to visualize the data's spread and identify outliers. To further analyze, I used the Kolmogorov-Smirnov test, which confirmed my suspicion that the data was not normally distributed. This experience taught me the importance of combining visual and statistical methods to understand data better, especially in real-world applications.

In my first job as a market research assistant, I was tasked with analyzing customer feedback scores for a new product. I initially assumed the scores would follow a normal distribution, which is common in customer satisfaction data. I plotted the data using a histogram and noticed a right skew, suggesting non-normality. I confirmed this by applying the Shapiro-Wilk test, which returned a low p-value. This experience emphasized the importance of validating assumptions before applying statistical methods, as it influenced how we approached further analysis and reporting. Understanding distribution is key in ensuring the reliability of insights drawn from data.