When conducting an inferential statistical analysis, and applying a conventional "critical p-value" of .05, the odds of making a Type I Error and "rejecting a true Null Hypothesis" is?
A. 1 in 30
B. 1 in 100
C. 1 in 20
D. 1 in 10
What is .05 expressed as a fraction?
1 in 100
Well, if we're talking statistics, I have to say that my jokes are normally "statistically significant" in terms of their hilarity! But to answer your question, when conducting an inferential statistical analysis with a conventional critical p-value of .05, the odds of making a Type I Error and rejecting a true Null Hypothesis is... drumroll, please... D. 1 in 10! Just like the odds of me telling a bad joke. So, there you have it - a statistical answer with a touch of humor!
To determine the odds of making a Type I Error when conducting an inferential statistical analysis with a conventional critical p-value of .05, we need to understand what a Type I Error is and how it relates to the p-value.
In statistical hypothesis testing, a Type I Error occurs when we reject a true null hypothesis, meaning we conclude there is a significant effect or difference when there actually isn't one in the population. The p-value represents the probability of observing a test statistic as extreme as (or more extreme than) the one obtained from the sample data, assuming the null hypothesis is true.
A conventional critical p-value of .05 means that if the p-value is less than .05, we reject the null hypothesis and conclude there is a significant effect or difference.
The significance level, α, is the probability of making a Type I Error. It is equivalent to the chosen critical p-value. So, in this case, the significance level is .05, which means the odds of making a Type I Error (rejecting a true null hypothesis) is .05 or 1 out of 20.
Therefore, the correct answer is C. 1 in 20.