Flashcards in Statistics Deck (69)

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## Study Power?

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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.

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## Nominal data

### In category, non-parametric

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## Ordinal data?

### In order, with unequal interval,non-parametric

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## Interval data?

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Equal interval

No absolute zero

Cannot compute ratio

parametric

Eg Tm in Celsius or Fahrenheit

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## Ratio data?

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Equal interval

with absolute zero or true zero

Can calculate ratio

parametric

Eg. Wt, hight, Kelvin Tm

"NOIR"

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## Measurement of central tendency?

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Mean

Median

Mode

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## Mean= Median = Mode, what distribution?

### Normal distribution

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##
Relationship of mean, median and mode in right (positive) distribution?

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Right skewed -Tail on the right

Mean>Median>mode

(Rule of thumb: mean always follows the tail)

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## The relationship of mean, median and mode in left skewed distribution?

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Tail is on the left of the distribution

Mean

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## For normal distribution, select statistic method?

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Select Parametric statistics test

Eg. Student t-test, chi-square, ANOVA, ANCOVA, regression analysis

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## 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

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## Ways of obtaining random sample?

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1. Simple random sampling

2. Systemic random sampling

3. Stratified random sampling

4. Cluster sampling

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## Bias?

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Systemic error

Impacts internal validity

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## Chance

### Radom

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## Confounder?

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Associated with exposure (risk) and outcome

An independent risk factor for the outcome

Not in the causal pathway between the risk factor and disease

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## Power

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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.

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##
When apply to a population,

Given sensitivity and prevalence,

True positive =?

False negative =?

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True Positive = Sensitivity x Prevalence

False negative = (1- Sensitivity) x Prevalence

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##
When apply to a population, given Specificity and Prevalence,

True negative =?

False positive =?

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True Negative = Specificity x (1- Prevalence)

False positive = (1- Specificity) x (1-Prevalence)

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## 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.

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## Baseline drift

### Which occurs with measurement on certain machines that requires frequent calibration.

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## Hawthorne effect

### A tendency among study subjects to change simply because they are being studied or watched.

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##
1SD =? %

2SD =? %

3SD =? %

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1 SD = 68% (Z score = 1)

2 SD = 95% (Z score = 2)

3 SD = 99% (Z score = 3)

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## When two events are independent, the probability of either will occur?

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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)

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## When two conditions are mutually exclusive, the probability that either one will occur is

### The sum of their probability

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## Randomization

### Assignment occurs by chance

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## ROC curve - Receiver-operator curve

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X axis: 1 - specificity, or the false - positive rate

Y axis: Sensitivity

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## ROC curve is used to determine

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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.

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## In ROC cure, the Area Under the Curve (AUC) is used to?

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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.

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## Kappa statistic

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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.

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