Question

Creating a study of your choosing, how would you consider false positives in your sample data and how would you account for them in your overall interpretation of the results of your study?

Answer 1

A false positive error is defned as a type I error where the test is checking a single condition, and wrongly gives an affirmative (positive) decision.

As a food blogger i have started a study of how my blogs are liked by readers.

here,the null hypothesis I will choose is:

“The number of times my blogs is read will be less or equal to the number of similar blogs I have posted”

If I reject the null hypothesis, this means one of two things.

1. This blogs performed above average — Great! There’s my positive outcome.

2. I have made a type I error. I rejected a null hypothesis that was true. My test showed that I performed above average, but in fact, I did not. I got a false positive.

Yes, here my false positive has a bad outcome, I would inevitably think my blog is better than it is and from then on write all my blogs in the same style, ultimately hurting my blog traffic. This will no doubt affect my career and self-esteem in a negative way.

There are some probability measures which helps us to decide false positive in our samble be:-

1.Alpha probability-the accepted risk of drawing a false‐positive conclusion in a single statistical test, which in most fields is arbitrarily set to α = 5%.

2.False‐positive report probability (FPRP)-the probability that a statistically significant finding is not true (FPRP = (α(1 − π)/[α(1 − π) + (1 − β)π], with α = Type I error probability, β = Type II error probability, π = the proportion of tested hypotheses that are true).