Answer:
Increased sample size Larger effect size Lower variability One-tailed test
Explanation:
The following combination of factors can increase the chances of rejecting the null hypothesis:
Decreased significance level (alpha): By choosing a lower significance level, such as 0.01 instead of 0.05, you make it more difficult for the results to be considered statistically significant, thereby increasing the chances of rejecting the null hypothesis.
Increased sample size: With a larger sample size, the test has more power to detect smaller effects or differences. This increased power can lead to rejecting the null hypothesis more often.
Larger effect size: If the effect or difference between groups being studied is larger, it becomes easier to detect and may lead to rejecting the null hypothesis.
Lower variability: If the data points in the sample are less spread out or have lower variability, it can increase the chances of rejecting the null hypothesis as the effect or difference becomes more evident.
One-tailed test: Conducting a one-tailed test instead of a two-tailed test can increase the chances of rejecting the null hypothesis. One-tailed tests focus on detecting a significant effect in one specific direction, whereas two-tailed tests consider the possibility of a significant effect in either direction.
It is important to note that these factors should be considered within the context of the specific hypothesis being tested and the statistical analysis being used. Additionally, the goal should be to design studies and analyze data in a way that ensures reliable and valid results rather than solely focusing on rejecting the null hypothesis.