Flashcards in Statistics Deck (69)
The power of a study is the probability of detecting a significant difference between treatments or study groups when there really is one.
Low power increases the likelihood of failing to identify a statistically significant difference when a real difference does exist.
High power (80% or more) is desirable .
Power is affected by sample size, etc.
In category, non-parametric
In order, with unequal interval,non-parametric
No absolute zero
Cannot compute ratio
Eg Tm in Celsius or Fahrenheit
with absolute zero or true zero
Can calculate ratio
Eg. Wt, hight, Kelvin Tm
Measurement of central tendency?
Mean= Median = Mode, what distribution?
Relationship of mean, median and mode in right (positive) distribution?
Right skewed -Tail on the right
(Rule of thumb: mean always follows the tail)
The relationship of mean, median and mode in left skewed distribution?
Tail is on the left of the distribution
For normal distribution, select statistic method?
Select Parametric statistics test
Eg. Student t-test, chi-square, ANOVA, ANCOVA, regression analysis
For non-normal distribution, eg. Bimodal, skewed, etc. test methods selection?
Non-parametric test eg.Fisher's exact test, McNemar test,Mann-Whitney U test, Wilcoxon's rank sum test, Kruskall-wallis test
Ways of obtaining random sample?
1. Simple random sampling
2. Systemic random sampling
3. Stratified random sampling
4. Cluster sampling
Impacts internal validity
Associated with exposure (risk) and outcome
An independent risk factor for the outcome
Not in the causal pathway between the risk factor and disease
The chance of finding an effect in your sample if it truly exist in the population.
Power is not a question in a study that shows a significant effects.
If a study results had failed to show a significant difference (p>0.05) between the two groups, one may wonder whether the study had sufficient power.
When apply to a population,
Given sensitivity and prevalence,
True positive =?
False negative =?
True Positive = Sensitivity x Prevalence
False negative = (1- Sensitivity) x Prevalence
When apply to a population, given Specificity and Prevalence,
True negative =?
False positive =?
True Negative = Specificity x (1- Prevalence)
False positive = (1- Specificity) x (1-Prevalence)
Regression toward the mean
In any group selected on a characteristic with substantial day-to-day variation, many will have values closer to the population mean when the measurement is repeated and worst pts will improve.
Which occurs with measurement on certain machines that requires frequent calibration.
A tendency among study subjects to change simply because they are being studied or watched.
1SD =? %
2SD =? %
3SD =? %
1 SD = 68% (Z score = 1)
2 SD = 95% (Z score = 2)
3 SD = 99% (Z score = 3)
When two events are independent, the probability of either will occur?
Is the sum of their probability, minus the probability that both will occur.
P (A or B) = P (A) + P (B) - P (A and B)
When two conditions are mutually exclusive, the probability that either one will occur is
The sum of their probability
Assignment occurs by chance
ROC curve - Receiver-operator curve
X axis: 1 - specificity, or the false - positive rate
Y axis: Sensitivity
ROC curve is used to determine
Optimal Cut-off point for the respective test.
In general, the point closest to the upper-left corner, where sensitivity is highest and the false-positive rate is lowest, is chosen as the cut-off.
In ROC cure, the Area Under the Curve (AUC) is used to?
To calculate the diagnostic accuracy (best sensitivity and specificity) of the test, that is the probability of correctly identifying disease based on the result of the test.
The larger the area under the curve, the better the test.
Used for reliability studies, eg to assess inter-rater reliability or intra-eater reliability.
Used in assessing the degree to which two or more raters, examine the same data, agree when it comes to assigning the data to categories.