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Sampling is crucial for making data analysis feasible in large datasets. Instead of analyzing all 1,000,000 responses, which is resource-intensive, a representative sample can be selected to draw meaningful conclusions. Sampling reduces complexity and costs while maintaining accuracy if the sample is well-chosen. For example, in customer feedback analysis, stratified sampling might be used to include responses across diverse demographics, ensuring the sample reflects the population's diversity. This allows timely, actionable insights without overloading resources. Why Other Options Are Wrong : A) Sampling doesn’t aim to analyze every data point; it focuses on representative subsets. C) Sampling can reduce errors but doesn’t eliminate them entirely, especially if biased. D) While sampling strives for representativeness, it cannot always guarantee 100% accuracy. E) Data validation remains necessary even in sampled datasets to ensure quality.
Which one of the following is the data distribution when mean and median values are same?
Which concept holds that consumers will not buy enough of organizations product unless it takes large scale selling and promotion effort?
The proportion of area under different crops at a point of time in a region is called:
Kranz anatomy type photosynthesis is shown by
A region with black soil experiences warm climate, while a neighboring region with red soil has comparatively cooler conditions. The reason th...
The maximum error degrees of freedom for same number of treatments and replication of an experiment can be achieved in
The pressure system is characterized by circular or elliptical isobars with the lowest pressure at the center is called
The Rhizobium species suitable for soybean crop is
Maximum CEC (cation exchange capacity) is of
_______is a fast-growing crop that is grown between successive plantings of a main crop.