here is the continuation Moreover, statistics often fail to capture the complexity of human behavior. For example, using test scores to measure student success may ignore contextual factors like motivation, emotional wellbeing, or home environment. Even if a statistically significant relationship exists between study hours and grades, it does not account for variables such as teacher quality or learning disabilities. Overlooking these factors can lead to misinterpretation or oversimplification of results. Another challenge involves misuse or manipulation of statistical data. Political polling is a well-known example where statistics can mislead. Poll results may exclude certain populations or rely on flawed assumptions, leading to skewed outcomes. Without full disclosure of sampling methods and margins of error, such statistics can present an inaccurate picture of public opinion. Lastly, not all research questions are suited to quantitative analysis. For research involving subjective experiences, cultural dynamics, or nuanced beliefs, qualitative methods may offer richer insights. In these cases, forcing statistical analysis may result in a loss of meaning or depth