A research team conducts an experiment to determine if a new cybersecurity tool is more effective than the previous version. What type of results are required for the experiment to be statistically significant?

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Statistical significance means the observed improvement is unlikely to be due to random chance. In other words, the data provide enough evidence to conclude there’s a real difference between the new cybersecurity tool and the previous version, not just a fluke from random variation. Reaching significance usually depends on having enough data to distinguish the true effect from noise and on setting an acceptable false-alarm rate, rather than relying on how fast results appear. Large sample size helps reduce sampling error and increase the ability to detect a real effect, and low variability can make it easier to see that effect, but these are means to achieve significance—not the definition itself. Rapid results don’t by themselves indicate significance.