Week 2 Flashcards
What is the simple confidence interval?
A range of values that we are confident contains the population parameter
What is point estimate?
A single value that represents the best estimate of the population value
In a confidence interval, the width concerns the ___ of the estimate
In a confidence interval, the width concerns the precision of the estimate
The point estimate is always in the ___ of the confidence interval
The point estimate is always in the middle of the confidence interval
What is the formal definition of a confidence interval?
If we repeated sampling an infinite number of times, 95% of the intervals would overlap the true mean
Not every value in a CI, is equally as ___
Not every value in a CI, is equally as probable
A more narrow confidence interval means that it is ____ precise
A more narrow confidence interval means that it is more precise
What are the factors that can narrow/increase a confidence interval?
- Larger sample size
- Less variance
- Lower selected level of
confidence (90% vs. 95%)
The null hypothesis is ___. And it states that _____
The null hypothesis is a sampling error. And it states that the population means(not sample means) are equal so the difference seen is not real
The alternative hypothesis states that the difference seen, represents __.
The alternative hypothesis states that the difference seen, represents a real difference.
What is a type 1 error in hypothesis testing? What is its symbol? This is considered a liar
When the null hypothesis is true, and we choose to reject it.
Symbol: “Alpha”
What is a type 2 error in hypothesis testing? What is its symbol? This is considered to be blind
When the null hypothesis is false, and we do not reject it. (accept it)
Symbol: Beta
___ is the maximum probability of type 1 error that a researcher is willing to accept
Alpha is the maximum probability of type 1 error that a researcher is willing to accept
When does the researcher set the alpha?
Set before running statistics
What is alpha usually set to?
0.05. (5%)
What is the simple definition of a p-value?
The probability of type 1 error if the null hypothesis is true
True or false.
You can have a probability of type 1 error what the null hypothesis is false
False
You can NOT have a probability of type 1 error what the null hypothesis is false
When is the p-value calculated?
After research
What is the formal definition of a p-value?
Probability of observing a value more extreme than actual value observed, if the null hypothesis is true
If the p-value is less than or equal to alpha, we ___ the null hypothesis
If the p-value is less than or equal to alpha, we REJECT the null hypothesis
If the p-value is greater than or equal to alpha, we ___ the null hypothesis
If the p-value is greater than or equal to alpha, we ACCEPT the null hypothesis
If we “fail to reject” (accept) Ho, we attribute any
observed difference to ____ only
If we “fail to reject” (accept) Ho, we attribute any
observed difference to sampling error only
We don’t interpret non-significant differences as “__”
maybe not even as “trends”
• We don’t interpret non-significant differences as “real” (maybe not even as “trends”)
We understand that a non-significant difference is
attributable only to __.
We understand that a non-significant difference is
attributable only to chance.
How do you use confidence intervals for hypothesis testing?
Look at the 95% CI of the mean difference, and evaluate whether or not it includes zero
If the confidence interval includes 0, it is ____ in hypothesis testing
If the confidence interval includes 0, it is nonsignificant in hypothesis testing
If the confidence interval excludes 0, it is ____ in hypothesis testing
If the confidence interval excludes 0, it is significant in hypothesis testing
What is the benefit of a CI over a p-value when hypothesis testing?
CIs give an estimate of effect size
P-values and CIs tells us about ___ not ____
P-values and CIs tells us about statistical significance not clinical significance
What is statistical power?
The probability of finding a statistically significant difference if such a difference exists in the real world
What are the main things that affect the statistical power of a study?
- Alpha
- Effect size
- Variance
- Sample size
Increasing alpha will ___ power
Increasing alpha will increase power
An effect size is known as the ____
An effect size is known as the mean difference
What is standardized effect size?
The mean difference divided by the variance
___ is the spread of scores
Variance is the spread of scores
Increasing the effect size will ___the power
Increasing the effect size will increase the power
Increasing the sample size will ___the power
Increasing the sample size will increase the power
___ is the best way to increase statistical power
Sample size is the best way to increase statistical power
Increasing variance will ___ power
Increasing variance will decrease power
What are the things that will decrease power?
- Decreased alpha
- Decreased effect size
- Increased variance
- Decreased sample size
What are the two types of power analysis?
- Power a priori
- Power post-hoc
What is power a priori?
A power analysis done before we collect data, to determine if the design is powerful enough
What is power post-hoc?
Power analysis done after the research is complete by the consumers to find if there was enough power/ if they failed to reject the null hypothesis
If a difference is found post-hoc/the null hypothesis was rejected, then the power issue is ___
If a difference is found post-hoc/the null hypothesis was accepted/fail to reject, then the power issue is moot/not a problem
If a difference not is found post-hoc/the null hypothesis was accepted/fail to reject, then the power issue is ___ and you have to do a ___
If a difference not is found post-hoc/the null hypothesis was accepted/fail to reject, then the power issue is huge and you have to do a post-hoc analysis
A priori is used to figure out how many subjects to use ___
A priori is used to figure out how many subjects to use before a study is started
What is the minimal accepted power during power a priori?
0.8
What are the 2 ways to determine a post doc analysis?
- Compute with traditional cohen approach
2. Determine with confidence interval analysis of effect size
What is involved in computing the post doc analysis with the traditional approach?
• Continuous scale result: 0.0 – 1.0 ( > 0.8 is default)
• Based on:
• Sample size
• Alpha
• Variance (observed)
• Effect size (use MCID, not
observed)____ is the better way to determine the post hoc analysis, while with ____, the answer will probably be the same as a priori
Determine with confidence interval analysis of effect size is the better way to determine the post hoc analysis, while with compute with traditional cohen approach, the answer will probably be the same as a priori
If the MCID is excluded from the CI, then it is definitively negative and ___ powered
If the MCID is excluded from the CI, then it is definitively negative and adequately powered
If the MCID is included from the CI, then it is not definitive and ___ powered
If the MCID is included from the CI, then it is not definitive and inadequately powered/ underpowered
A two tailed testis testing to see ____
A two tailed testis testing to see if your calculated value is either above or below where it is expected to be
A one tailed test is testing to see if ____ or ___
A one tailed test is testing to see if your calculated value is above where it’s expected to be or below where it is expected to be
___ is the assumption you’re beginning with and is opposite of what you’re testing
Null hypothesis(H0) is the assumption you’re beginning with and is opposite of what you’re testing
___ is the claim you’re testing
Alternating hypothesis is the claim you’re testing
