Statistical Interrogation and Python

This week, I've focussed my studies on learning the fundamentals of Python programming. It's been a slow but steady process, with early morning and/or evening study sessions whenever possible. I already had the opportunity to apply some of my newly acquired knowledge during this week’s workshop which focused on statistical interrogation. I explored the five essential steps required to validate a statistical hypothesis test and coded the entire process using Python.

Statistical hypothesis testing is a fundamental method used in statistics, it involves formulating two competing hypotheses:

-The null hypothesis (H0), which assume that there is no effect or difference,

-The alternative hypothesis (H1), which suggests that there is an effect or difference.

Researchers collect data and perform a statistical test to determine whether to reject the null hypothesis in favor of the alternative. To be honest, it's easier to express this concept through a mathematical lens than in English. This process includes calculating a test statistic, which is then compared to a critical value or used to derive a p-value.

If the p-value is less than a predetermined significance level (commonly set at 0.05), the null hypothesis is rejected, indicating that the observed data is statistically significant.

The most fascinating aspect was translating this theoretical knowledge into a practical Python program. The tutor mentioned that the same process can be done using Excel, but I found it much cooler and simpler to implement in Python and use the dedicaded Python's library.

Moreover, I managed to find time to complete the module on Reflective Models and write my own reflection example using the Kolb Model of Reflection.

This model is structured by the following steps:

- Concrete Experience: Engaging in a specific experience.

- Reflective Observation: Reflecting on the experience.

- Abstract Conceptualisation: Drawing conclusions and learning from the experience.

- Active Experimentation: Applying what was learned to new situations.

I also completed the final part of the aptitudinal tests and delved into some of the suggested lessons to improve my knowledge. To my pleasant surprise, I received a certification of achievement in my inbox upon completion.

Next week may be the perfect opportunity to relax and enjoy the holidays, as our next planned workshop is not until 27th August. Therefore, it might not be worthwhile to write and post any articles. Instead, I will be focusing on writing, or at least structuring, the content for some of the marked assignments that need to be completed by mid to late September, while also continuing my Python studies.