# Statistical Tests

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• ## Figure out the variable type

• Scales of measurement (qualitative or quantitative)
• ## Figure out your goal

• Compare groups
• Measure relationship or association of variables

• ## Examples:

• Gender
• Race
• Marital status
• Whether or not tumor recurred

• ## Examples:

• Cancer stages
• Apgar scores
• Pain ratings
• Likert scale

• ## Cannot multiply & divide values

• No true zero point
• ## Example:

• Temperature on a Celsius scale
• 00 indicates point when water will freeze, not an absence of warmth

• ## Quantitative data with true zero

• Can add, subtract, multiply & divide
• ## Examples:

• Age
• Body weight
• Blood pressure
• Length of hospital stay
• Operating room time

• ## Descriptive

• Frequencies & percents
• Measures of the middle
• Measures of variation
• ## Inferential

• Nonparametric statistics
• Parametric statistics

• ## Median

• 50th percentile score
• half above, half below
• Use when data are asymmetrical or skewed

• ## Standard deviation (SD)

• Square root of the sum of squared deviations of the values from the mean divided by the number of values
• ## Standard error (SE)

• Standard deviation divided by the square root of the number of values

• ## Variance

• Square of the standard deviation
• ## Range

• Difference between the largest & smallest value

• ## Parametric tests

• Used for analyzing interval & ratio variables
• Normal distribution
• Homogeneity of variance
• Independent observations

• ## Step 2 Know your goal

• Is it to compare groups? How many groups do I have?
• Is it to measure a relationship or association between variables?

• ## Chi-Square

• Fisher’s exact test

• Unpaired
• Paired

• ## p < 0.01

• 1 in 100 or 1% chance of error
• ## p < 0.001

• 1 in 1000 or .1% chance of error

• ## Research question hypothesis

• Null hypothesis (H0)
• Predicts no effect or difference
• Alternative hypothesis (H1)
• Predicts an effect or difference

• ## Use to test the difference between observed & expected proportions

• The larger the chi-square value, the more the numbers in the table differ from those we would expect if there were no association
• ## Limitation

• Expected values must be equal to or larger than 5

• ## Fisher’s exact test

• Is based on exact probabilities
• Use when expected count <5 cases in each cell and
• Use with 2 x 2 contingency table

• ## Test for a difference between groups

• Is the difference in sample means due to their natural variability or to a real difference between the groups in the population?

• ## Test for a difference between groups

• Is the difference in sample means due to their natural variability or to a real difference between the groups in the population?

• ## Assumptions:

• Variables are normally distributed
• Relationship is linear
• Both variables are measured on the interval or ratio scale
• Variables are measured on the same subjects

• ## Appropriate interpretation of statistical test

• Group differences
• Association or relationship
• “Correlation does not imply causation”

## Don’t Lie With Statistics !

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