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For a given experiment, and a given sample size, there is a probability that a treatment effect of a given size will yield a statistically significant finding. That is, if the treatment effect is 1 unit, then that probability (the power) might be 50%, and the power for a treatment effect of 2 units might be 75%, etc. Unfortunately, before the experiment, we don't know the treatment effect size, and indeed after the experiment we can only estimate it.
So a statistically significant result means that, whatever the treatment effect size happens to be, Mother Nature gave you a "thumbs up" sign. That is more likely to happen with a large effect than with a small one.
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Why are the larger t-ratios more likely to be statistically significant?
Larger t-ratios indicate a greater difference between the sample mean and the null hypothesis mean relative to the variability in the data. This suggests that the observed effect is less likely to be due to random chance. As a result, larger t-ratios are more likely to exceed the critical value for significance, leading to a higher probability of rejecting the null hypothesis. Thus, they often indicate stronger evidence against the null hypothesis.
Is this a probability or statistic the incidence of a particular side effect of a new drug is 15 percent?
It is a probability; probability of side effect is .15 and probability of no side effect is .85.
What can be used to show a cause and effect between two variables?
A controlled experiment can be used to show a cause and effect relationship. ex: an experiment studying the effect of a certain medicine on patients.
Correlational study is it cause and effect?
No correlational study is not cause and effect because correlation does not measure cause.
What is strong correlation?
A very small effect having a greater side effect on a variable or an object may be termed as a strong correlation.
