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The statement is true because the running time of an algorithm is indeed indicative of the number of instructions that the algorithm executes when handling a specific instance or input. This is a critical concept in algorithm analysis, as it provides a way to evaluate how efficient an algorithm is in terms of time complexity.
When measuring the running time, we typically consider factors such as the size of the input and how the algorithm's performance scales with larger inputs. Running time can be affected by the specific instance being processed, such as the arrangement of data in sorting algorithms.
This perspective is essential for understanding performance in the context of specific conditions, aiding in the selection and optimization of algorithms for various problems. It highlights the fact that running time is not just a mere count of executed commands but also encompasses the efficiency and effectiveness of the algorithm for different inputs.
While other options suggest variability or uncertainty about this definition, the notion that running time correlates directly to the instruction count when an algorithm is executed on a given input instance holds true.