Final answer:
A study with a level of significance of 0.05 is more likely to have a Type I error compared to a study with a level of significance of 0.01, because the larger significance level increases the probability of incorrectly rejecting the true null hypothesis.
Step-by-step explanation:
When comparing a study with a level of significance of 0.05 with a study having a level of significance of 0.01, the researcher level of significance of 0.05 is more likely to have a Type I error. A Type I error occurs when the null hypothesis is falsely rejected. The level of significance, often denoted as alpha (α), is the threshold for rejecting the null hypothesis. Since a level of significance of 0.05 is larger than 0.01, it translates to a higher probability of making this type of error.
The Type I error corresponds to concluding that there is an effect or a difference when in fact there is none; for instance, saying a drug works when it actually does not. As the level of significance increases, so does the likelihood of a Type I error, because you are more willing to reject the null hypothesis. Conversely, a lower alpha level, such as 0.01, would mean that you require more evidence before you decide to reject the null hypothesis, thus potentially reducing the chance of this error.