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Statistics involves the analysis of both quantitative and qualitative data. Quantitative data refers to numerical information that can be measured and quantified, such as heights, weights, temperatures, and scores. This type of data enables statisticians to perform mathematical calculations to identify trends, averages, and variances, providing a clear picture of the data through numerical representations.

On the other hand, qualitative data consists of non-numerical information that captures attributes, characteristics, or categories, such as colors, names, or opinions. This type of data is crucial when analyzing patterns, themes, and concepts within the information being studied. Qualitative data is often analyzed through categorization or thematic analysis, helping researchers understand the underlying reasons or motivations behind certain behaviors or trends.

By being able to analyze both types of data, statisticians can gain a more comprehensive understanding of the subject matter, allowing for robust conclusions and more effective data-driven decision-making.