Checking for statistical significance when testing a new marketing channel ensures:

You developed a strong hypothesis.

You’ve chosen the correct KPIs.

You achieve similar results next time.

Your hypothesis is correct.

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Explanation: The correct answer is ‘You achieve similar results next time.’ Checking for statistical significance when testing a new marketing channel is crucial because it ensures that the results observed are not due to random chance or variability. Statistical significance indicates that the observed differences in performance between the new marketing channel and existing ones are likely to be genuine and replicable. By achieving statistical significance, marketers can have confidence that the insights gained from the test are reliable and can be used to inform future decisions and strategies. This approach promotes data-driven decision-making and increases the likelihood of achieving consistent results when implementing similar initiatives in the future. Additionally, statistical significance provides a level of assurance that resources are allocated effectively, minimizing the risk of investing in ineffective marketing channels or strategies. Therefore, ensuring statistical significance is essential for optimizing marketing efforts and maximizing ROI over time.

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