What does dispersion refer to in a sample?

Dispersion in a sample refers to the variability or spread of data points around a central tendency, such as the mean. It provides insight into how much individual data points differ from one another and from the average value. High dispersion indicates that the data points are spread out over a wide range, while low dispersion suggests that they are clustered closely around the mean.

When analyzing data, understanding the degree of dispersion is crucial as it affects interpretations of consistency and reliability within the sample. Identifying variability helps in assessing risk, making predictions, and informing decision-making processes, as it highlights how much the data can be expected to fluctuate.

The other choices, while important in their own contexts, do not relate directly to the concept of dispersion in data analysis. Accuracy of measurements pertains to how close a measurement is to the true value but does not describe how spread out data values are. Cost efficiency and time management are focused on resource optimization and project timeline concerns, which are not applicable in the context of assessing data variability.