In statistical analysis, inference plays a crucial role as it involves drawing conclusions about a larger population based on a sample of data. Despite its counterintuitive nature, the standard practice is to attempt to disprove a research claim that is not of interest. For instance, when comparing the effectiveness of two medical treatments, we initially assume that both treatments yield equal survival rates, only to later disprove this assumption using the data. Furthermore, the concept of a p-value is introduced, which quantifies the level of disagreement between the data and the hypothesis. This statistical measure helps determine the strength of evidence against the null hypothesis. Additionally, we delve into the concept of confidence intervals, which provide an estimate of the magnitude of the effect of interest. For example, confidence intervals can indicate how much better one treatment is compared to another. Overall, these foundational aspects of statistical analysis, including inference, p-values, and confidence intervals, are essential tools for drawing meaningful conclusions from data and evaluating the significance of research claims.