The researchers conducted a Chi-square Goodness of Fit Test to examine the distribution of a dataset related to conspiracy theories about the harmful health-related side effects of COVID-19 vaccines. They expected an equal distribution. However, based on their analysis, they rejected the null hypothesis, indicating that the social-political context is the primary driving factor behind these conspiracy theories. They compared the obtained Chi-square statistic to the critical Chi-square value from the Chi-square Distribution Table, considering a significance level of 0.05 and 5 degrees of freedom. They found that the obtained Chi-square statistic exceeded the critical value of 11.070, indicating that the difference between the observed and expected values is statistically significant.

The researchers conclude that social-political context is the primary driving factor behind these conspiracy theories as proven by the Chi-square Goodness of Fit test and as can be seen in the Data Modelling. However, it’s also important to not ignore the other tackled contexts such as Economic and Racial ones that are still present in our dataset and most likely, in the entire social media. 

The researchers would like to extend their sincere appreciation and heartfelt thanks to Sir Paul Regonia for his invaluable guidance and expertise throughout the duration of this Data Science Project. His unwavering support and extensive knowledge have been instrumental in our accomplishments, enabling us to delve into the local contexts and explore the various conspiracy theories surrounding the potential adverse health side effects of COVID-19 vaccines. Without his constant presence and mentorship, our achievements would not have been possible.

[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8441835/
[2] https://www.icjournal.org/pdf/10.3947/ic.2022.0012
[3] https://www.scribbr.com/statistics/chi-square-goodness-of-fit/
[4] https://www.scribbr.com/statistics/chi-square-distribution-table/