Homework Help: Questions and Answers: 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?
a) Results that are unlikely to occur again
b) Results that are real and not caused by random chance
c) Results that are hypothetical and in need of more testing
d) Results that are inaccurate and should be ignored
Answer:
First, let’s understand statistical significance:
Statistical significance refers to the likelihood that the results of an experiment are not due to random chance. It is a way to determine whether the findings of a study are reliable and can be generalized to a broader population.
Given Options: Step by Step Answering
a) Results that are unlikely to occur again
- This suggests that the results are rare or not reproducible, which is not a characteristic of statistically significant findings. Statistically significant results should ideally be replicable.
b) Results that are real and not caused by random chance
- This directly matches the definition of statistical significance, where the results reflect a true effect and are not due to random variability.
c) Results that are hypothetical and in need of more testing
- Hypothetical results indicate that the findings are still speculative, which doesn’t align with statistical significance, as it refers to actual, tested results.
d) Results that are inaccurate and should be ignored
- Inaccurate results would not be statistically significant, since they would not represent a reliable finding.
Final Answer
Based on the above analysis, the correct answer is
b) Results that are real and not caused by random chance.
This means that if the new cybersecurity tool shows statistically significant improvement over the previous version, the researchers can be confident that the observed difference is likely due to the tool’s effectiveness rather than random variation.
Learn More: Homework Help
Q. In which cases deep learning is preferred over machine learning?