**TYPE OF ERRORS**

**Your Hypothesis:**difference exists between A and B

**Null Hypothesis:**(Contradicts your Hypothesis) there is no difference between A and B

**Type I Error:** Incorrectly rejecting the null hypothesis

- (The study showed that there is difference but in fact there is not difference, you think your study was successful but in fact it wasn’t!)
- Type I Error = False Positive
- p-value: chance of making type I error
- (p<0.05 = <5% chance of commenting this error)

**Type II Error:** Incorrectly accepting the null hypothesis

- (The study showed that there is no difference but in fact there is difference, you think your study was not successful but in fact it was!)

- Type II Error = False Negative

- Increasing the power (bigger sample size) decreases this error

**Type III Error:** Conclusions not supported by data

**95% confidence interval:** If it includes de value 1, it is not statistically significant.

- The farther away form 1 the stronger the correlation (i.e., 9-10 or 0.1-0.2 has stronger correlation than 2-3 or 0.8-0.9)

**Prevalence:** # of patients having the disease in the population.

- (It’s higher in long lasting diseases)

**Incidence:** # of newly diagnosed cases in a population in a given time period of time

**SCREENING AND DIAGNOSTIC TESTS**

**Sensitivity:**(analyzes the tests results (+) or (-) in the Patients with the disease/condition)

- TP / (TP + FN) True positive rate:

- The probability that a patient with the disease will have a positive test result.

- SnOut: a sensitive test with a
**(-)**result its good at ruling-out the disease

- SnOut: a sensitive test with a

- (You can trust Negative results)

- High Sensitivity = Low False Negatives

**Specificity:**(analyzes the tests results (+) or (-) in the Patients without the disease/condition)

- TN/(TN+FP) True negative rate:

- The probability that a patient without the disease will have a negative test result

- SpIn: a highly specific test with a
**(+)**result its good at ruling-In the disease

- SpIn: a highly specific test with a

- (You can Trust Positive results)

- High specificity = Low False Positives

Predicted Values are dependent on the prevalence of the disease:

**Positive Predict Value:**The probability that a person with a positive test result actually has the disease.

- (Prevalence is directly proportional to PPV)

**Negative predictive value:**The probability that a patient with a negative test result really is free of the disease.- (Prevalence is inversely proportional to NPV)

**Accuracy:** (TP+TN)/(TP+TN+FP+FN)

**Prevalence:** (TP+FN)/(TP+TN+FP+FN)

**STUDIES/DESIGNS:**

**Case Control:**Retrospective

- Takes patients with the disease and look in the past to see what factors contributed to develop the disease.

- Uses Odds Ratio for the calculations: (TPxTN)/(FPxFN)

**Cohort study:**Prospective- Takes a group of pts exposed to a risk factor and a group of pts not exposed and follows them up for a couple of years to see how the disease develops, or if a drug has effect or not.
- Uses Relative Risk for the calculations:
- Incidence in exposed/incidence in unexposed
- (TP/(TP+FP))/(FN/(FN+TN))

**Clinical Trial:** Randomized, Double blind, Multicenter, Placebo, control.

**Meta-analysis:******Review and statistical- Combining of data from different studies
- (Increases the power of any single study)
- Also use (also uses Odds Ratio)

**STATISTICAL TESTS:**

**Quantitative:****T test:**Compares 2 groups (ex: means of weight b/t 2 groups)**ANOVA:**Is a t-test for more than 2 groups.

**Qualitative:**

- '
*'***Non-parametric statistics:**for qualitative data analysis. Race, sex, medical problems and diseases, medications)

- '

**Chi-square:**compare 2 groups with categorical variables (obese patients with diabetes Vs. Obese patients without diabetes

**Kaplan-Meyer:**(small groups) estimate the survival rate

**STATISTICAL TOOLS:**

**Mean:**

- The average of the Test

- Central tendency in a Normal Distribution

- The confidence interval of the mean gives the answer

**Variance:** The spread of data around the mean

**Median:**The middle value of a set of data.

- Central tendency in a NON normal Distribution

**Mode:**The most frequent occurring value

Example: 2,3,5,5,7,8,9,11,12

Mode=5, Mean=6.8, Median= 7