5 Statistics You Should Know Being Smart Reading Statistics Martha J Lentz Funding Support : SON Office for Nursing Research P30 NR04001
So What are the Stats Effect size Plots/Descriptive Correlations Tests of Difference Time ordered
How do I know which one to use? What is the question
Types of Questions Look for action verb Describe Compare Association among Count the number of groups What kind of dependent variable
So What are the Stats Effect size Plots/Descriptive Correlations Tests of Difference Time ordered
Effect Size Lets us know was the sample big enough Mean1 – Mean2/ sd Do try this at home
Find These Effect Sizes d Sample 12 women POMS Anger Scale mean1=2.25 mean2=1.44 sd=2.84 d=0.28 Bodily Feeling Muscle Pain mean1=17.17 mean2= 15.0 sd 1.46 d=1.49
Rule of Thumb Effect Sizes Small = .2 Medium = .5 Large = .8
Sample Per Group Need for 80% Power Effect Size .2 .3 .5 .6 .7 .8 1.0 1.40 Sample 393 175 64 45 33 26 19 10
Statistical Significance is not Clinical Significance A non statistically significant effect may be clinically important
So What are the Stats Effect size Plots/Descriptive Correlations Tests of Difference Time ordered
Plots Plot your data Look at plots in papers, do they really look like what author is claiming.
Scatter Plot HT vs. WT
Line Plot eNo Does the child have a diagnosis of asthma?" baseline night no yes sleep delay night 30.00 25.00 20.00 Mean eNO 15.00 10.00 5.00 usual UB+4 hrs UB+8 usual UB+4 hrs UB+8 bedtime hours bedtime hours (UB) (UB) Comparison
Error Bars eNO Clustered by Group labprot 35.00 baseline noc sleep delay noc 30.00 95% CI Average eNO_2 25.00 20.00 15.00 10.00 5.00 no yes Does the child have a diagnosis of asthma?"
Descriptive Mean Median SD
Look at sd Compared to Mean Sample 12 women POMS Anger Scale mean1=2.25 mean2=1.44 sd=2.84 Bodily Feeling Muscle Pain mean1=17.17 mean2= 15.0 sd 1.46
So What are the Stats Effect size Plots/Descriptive Correlations Tests of Difference Time ordered
Correlation Bivariate- between two variables Regression- several variables to one outcome variable Need variability
Scatter Plot HT vs. WT r=.72 r 2 =.52
Rule of Thumb Little r < 0.4 is not really meaningful Square r and look at % variance explained <16% not really meaningful Regression look at change R 2 apply above rule
So What are the Stats Effect size Plots/Descriptive Correlations Tests of Difference Time ordered
Tests of Difference How many independent groups Parametric test- estimate parameter e.g. a mean T-test two groups ANOVA multiple groups Non-Parametric Mann-Whitney U two groups Kruskal-Wallis multiple groups
Parametric or Non-Parametric Use Parametric when Have appropriate distribution required to estimate a parameter such as mean Use Non-Parametric when Have a funky distribution Need to do calculation by hand
Good Distribution Use Parametric Total score: Attitude Toward Women Scale 80 60 Frequency 40 20 Mean =120.63� Std. Dev. =17.189� N =652 0 50.00 75.00 100.00 125.00 150.00 Total score: Attitude Toward Women Scale
Not So Good Use Non-Parametric Histogram 5 4 Frequency 3 2 1 Mean =115.83� Std. Dev. =8.211� N =12 0 100.00 110.00 120.00 130.00 140.00 total bodily feel score noc 5
Another Reason to Plot Your Data
So What are the Stats Effect size Plots/Descriptive Correlations Tests of Difference Time ordered
Time Ordered Data It is correlated It violates all assumption of independence of most stats It is very hard to get don’t waste it
Time Ordered Data Types of Tests Paired T-Test 2 points in time Repeated Measure ANOVA multiple points in time More then 4 need special time series tests
Time Ordered Data Abuse It is flat out wrong to treat each time point as a separate subject and use in a conventional statistical test Look at sample N if few in number and df or n listed for test is a lot it is wrong
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