Final Exam Flashcards
How to calc variance and std dev
- Find diff b/n ea value and the mean; square this difference
- Add up all the squared diff and divide by total # of values
- Std Dev= Square root of variance
FINER
- Feasibility
- Interest
- Novel
- Ethical
- Relevant
PICOT
- Population
- Intervention
- Control
- Outcomes
- Time
Categorical Variable (2 types)
- Nominal (no order) v ordinal (high/med/low)
- Use percentages and frequencies for central tendency
Skewed Distributions
- Neg/L - more values on high end
- Pos/R - more value on low end
**if skewed use range and IQR for measure variance NOT variance and SD
Central Limit Theorem and Confidence Intervals
- If we take repeated random samples from our population, calculate each sample mean , and plot out those sample means, then:
- Mean of sample means = population mean
- Std error=std dev of sample means (std error=sample SD/square root of sample size)
- Confidence Intervals- range of numbers that population mean will likely be within x% probability given observed sample mean and size
- 95% CI is approx sample mean +/- 2SE
- 95% CI is wider than a 90% CI
What does p-value mean?
- Probability of observing difference this extreme or more given null hypothesis is true
- If value is sufficiently small, then reject null hypothesis and accept alternative hypothesis
NOT the probability that the null is true OR that 1-p represents probability that alternate is true
3 Ways to Determine Stat Sig
- P-value below cutoff? (.05 normally)
- Test statistic exceeds the critical value
- Confidence interval of desired probability EXCLUDE 0 (group difference) or 1 (group ratio)
- If 2 groups are the same (null is true) then the diff b/n there means would be 0 and the ratio between their means would be 1
Parametric v Non-parametric
- Parametric assume dependent variable is normally distributed so…
- Takes advantage of known properties of a distribution , allows for efficiency (less subjects, detection of smaller effect size ), allows for effect estimation ( i.e. confidence interval of group effect )
- Null Hypo- no diff b/n means of groups
- Non-parametric - dependent variable is not normally distributed or too few observations to assume so…
- Based on ranks (observations ordered from high to low - ties receive avg ranks)
- Null Hypo- no diff b/n dist of ranks b/n groups
- Advantages: Less requirements , useful for dealing with outliers , intuitive , useful for certain categorical data
- Disadvantages: less efficient , hypothesis testing over effect estimation , too many rank ties problematic
Chi Square
dependent and independent are categorical
non-para equivalent is Fisher Exact
T test
cont dependent variable and 2 category indep variable + equal variance b/n groups
non-para equivalent is Mann Whitney
Paired t Test
same as t test but observations/dependent variables can be paired
non-para equivalent is Wilcoxon Signed Rank Test
ANOVA
cont dependent variable and 3+ category independent variable
non-para equivalent is Kruskal Wallis Test
Pearson Correlation
normally distributed/cont dependent and independent variable
non-para equivalent is Spearman Correlation
Linear v Logistic Regression
- Linear - cont dep var
- Output = can determine stat sig of ea independent var b/c gives you coefficient (pos or neg) and CI for ea
- CI should not include 0 b/c group difference
- Logistic- dichotomous dep var
- Output= given in odds ratio (which can then be converted to probability) and CI for ea independent var
- CI should not include 1 b/c group ratio
Deductive v Inductive Reasoning
Deductive reasoning (does pt fit pattern?) -start w disease
Inductive reasoning (what pattern does this pt fit?) - diff diagnosis
