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Below are the are the potential pitfalls or errors that could occur during the analysis when wevuse an LLM like ChatGPT to analyze whether there is a significant difference between two sets of continuous data 1. Firstly, LLMs may not understand which statistical characteristic is being analysed if it’s the mean, median ..etc 2. Direct analysis may be performed by LLMs without considering the Prechecks prior to analysis such as detecting outliers or handling missing values, may not be performed automatically leading to misleading results 3. LLMs may lack context and there are chances of choosing inappropriate alpha value. Choosing a smaller alpha value is critical specially in high-stakes scenarios an example can be of drug testing, where the continuous data might require a smaller alpha value to minimize Type I Errors 4. LLMs may also fail to differentiate between statistical tests required to be performed whether a paired t-test (used for comparing the means of two related groups) or an independent t-test (used for comparing the means of two independent groups) is appropriate for the given data. 5. Validating assumptions before performing any statistical test, such as checking for normality or equal variance, is another area where LLMs may fall short. While they may provide numerical summaries in response, they often do not generate the graphical summaries necessary for a thorough validation 6. Additionally, LLMs might proceed with a non-parametric test without verifying whether the data actually requires it. applying them unnecessarily can result in less powerful or less meaningful analysis, particularly when parametric tests are suitable for the data.