
Understanding P-Hacking and Statistical Significance

Interactive Video
•
Mathematics, Science
•
9th - 12th Grade
•
Hard

Amelia Wright
FREE Resource
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10 questions
Show all answers
1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main concern with p-hacking in statistical research?
It is a recommended practice in research.
It is a foolproof method to find true effects.
It can create false impressions of significance.
It always leads to accurate results.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In null hypothesis significance testing, what does it mean to 'fail to reject the null'?
The null hypothesis is proven true.
There is no evidence to reject the null hypothesis.
There is strong evidence against the null hypothesis.
There is no evidence to support the null hypothesis.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why might researchers be tempted to engage in p-hacking?
To increase the sample size.
To ensure their research is published.
To simplify their research process.
To avoid any statistical errors.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the family-wise error rate?
The chance of all tests being significant.
The likelihood of at least one Type I error in multiple tests.
The error rate in a single statistical test.
The probability of making a Type II error.
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In the jelly bean example, what was the main issue with conducting multiple tests?
It confirmed the initial hypothesis.
It ensured all results were accurate.
It decreased the overall error rate.
It increased the chance of finding a false positive.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What was the outcome of the Cornell food science lab's study on buffet pricing?
All results were published without issue.
The study confirmed the initial hypothesis.
The study was praised for its methodology.
Some papers were retracted due to p-hacking.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How can researchers correct for inflated error rates in multiple tests?
By ignoring non-significant results.
By using a Bonferroni correction.
By increasing the sample size.
By conducting fewer tests.
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