(184 words)
While I had already done some statistics in an earlier Numerical Analysis module, this unit helped improve my confidence in statistical reasoning. Working with summary measures improved my confidence in interpreting measures such as the mean, median, and interquartile range. With the example of income distribution among the male and female sexes, I see statistics not only as a tool for analysis but also as a way to shape understanding and inform policy.
Conducting t-tests and interpreting p-values was a bit strange in Excel, as I had previously done them using R. In the context of my research on AI in healthcare, understanding inferential tools will help me to test claims with greater rigour.
The second collaborative discussion highlighted how similar data can be framed to support different narratives, which brought me back to the ethical questions raised in earlier units. I now understand the importance of being transparent about all results, not just those that appear significant. These statistical tools will be essential in evaluating data from future studies, and this unit helped bridge the gap between numbers and meaning in real-world contexts.