Biomeasurement Continued from last week: t- and Mann-Whitney U tests Background reading: Chapter 7 .
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Transcript of Biomeasurement Continued from last week: t- and Mann-Whitney U tests Background reading: Chapter 7 .
Biomeasurement
Continued from last week:
t- and Mann-Whitney U tests
Background reading: Chapter 7
www.biomeasurement.net
Lecture Content• Comparing t- & MWU tests
• t-test
• Mann Whitney U test
Mann-Whitney U Test
• Comparison to t-Test
• When to use
• Example data
• Four steps
• Example from literature
Bone Density(g/cm2)
Females Males
0.972 0.9050.732 1.0160.874 0.8730.943 0.741.024 0.8610.755 0.8170.779 0.8971.007 0.9620.816 0.8510.755 0.8210.871 0.7630.721 0.8760.727 0.9440.796 0.9930.612 0.7740.775 0.7850.849 0.8920.773 1.0760.649 0.8880.865 0.865
Brine Shrimp Length (mm)
MediumSalinity
HighSalinity
5.5 6.06.0 7.05.0 7.57.0 6.05.5 7.56.0 8.07.0 11.08.0 9.06.0 8.08.0 11.06.0 8.07.0 8.06.0 7.07.0 7.06.0 7.08.0 9.06.0 .7.0 .7.5 .6.0 .7.5 .
• Difference
• Two samples
• Unrelated
SimilaritiesBirth weight (kg) of babies born to
Non smokers Heavy smokers
3.99 3.183.79 2.843.60 2.903.73 3.273.21 3.853.60 3.524.08 3.233.61 2.763.83 3.603.31 3.754.13 3.593.26 3.633.54 2.383.51 2.342.71
Differencest-test• Parametric• Scale data only
Mann-Whitney U test• Nonparametric• Scale or ordinal data
Speed of swimmingMedium Salinity High
Salinity
Slow FastFast SlowFast Very Fast
Medium FastSlow Medium
Very slow Very slowVery Fast SlowVery Slow Very Fast
Slow Very FastFast Very Fast
Medium FastSlow VerySlow
Time spentPlaying
(hours/day)
Enrichment Control
4.5 3.54.3 6.35.3 7.86.4 4.34.9 4.63.7 5.15.2
When to use• Difference.
• Two samples.
• Unrelated data.
• Dependent variable: Scale level
Do not use if...
• Comparing frequency distributions.
Bone Density(g/cm2)
Females Males
0.972 0.9050.732 1.0160.874 0.8730.943 0.741.024 0.8610.755 0.8170.779 0.8971.007 0.9620.816 0.8510.755 0.8210.871 0.7630.721 0.8760.727 0.9440.796 0.9930.612 0.7740.775 0.7850.849 0.8920.773 1.0760.649 0.8880.865 0.865
Example Data
Tests on counts/frequencies Tests of Relationship
Tests of Difference
1 set of Categories 2 sets of Categories
For scale/ordinal data from two variables
Regression Correlation
Scatterplot ScatterplotOnePie Chart
TwoPie Charts
Parametric Nonparametric
For counts/frequencies in categories
For scale/ordinal dependent variable data in categories distinguished by the independent variable
©Hawkins & Carter 2004
Pie Charts
Errorplots or Boxplot
Errorplots Boxplots
Choosing Chart
for Graphs
Scatterplots
The Four Steps
1. Construct a Null Hypothesis (Ho).
2. Decide Critical Significance Level ).
3. Calculate Statistic.
4. Reject or Accept the Null Hypothesis.
1. Construct Ho
Ho: There is no difference between the bone density of males and females over 50 years old.
In general no difference between the samples.
2. Decide
5% = 0.05.
3. Calculate Statistic.
U
U1 = n1 n 2 + n2(n2 + 1) - R2
2
U2 = n1 n 2 + n1(n1 + 1) - R1
2
Sum of ranks of sample 2
Size of sample 1
U is the the lower value of U1 or U2.
Check: U1 + U2 = n1 n 2
Bone Density Measurement (g/cm2)Females Males
0.972 35 0.905 310.732 5 1.016 380.874 26 0.873 250.943 32 0.740 61.024 39 0.861 210.755 7.5 0.817 170.779 13 0.897 301.007 37 0.962 340.816 16 0.851 200.755 7.5 0.821 180.871 24 0.763 90.721 3 0.876 270.727 4 0.944 330.796 15 0.993 360.612 1 0.774 110.775 12 0.785 140.849 19 0.892 290.773 10 1.076 400.649 2 0.888 280.865 22.5 0.865 22.5
330.5 489.5R2
R1
More about R
U1 = 20x20 + 20(20 + 1) – 489.5 2 =120.5
U2 = 20x20 + 20(20 + 1) – 330.5 2 =279.5
U
U = 120.5
n1 = 20
n2 = 20
Bone Density(g/cm2)
Females Males
0.972 0.9050.732 1.0160.874 0.8730.943 0.741.024 0.8610.755 0.8170.779 0.8971.007 0.9620.816 0.8510.755 0.8210.871 0.7630.721 0.8760.727 0.9440.796 0.9930.612 0.7740.775 0.7850.849 0.8920.773 1.0760.649 0.8880.865 0.865
Using SPSS
Dependent Variable
Independent Variable
4. Reject or Accept.
Using Critical Values.
Reject if your t is bigger than tcritical.
Using P Values
Reject if P is less than or equal to
Where do you get these from?
Using Critical Values.
120.5< 273 Reject
Bone density between males & females over 50 is different
From...
Step 2: = 0.05
Step 3: U=120.5, n1=20, n2=20
If U </= Ucritical REJECT H0 significant result.
n1
1 2 3 4 5 16 17 18 19 20n2 1 - - - - - - - - - -
2 - - - - - 31 32 34 36 383 - - - - 15 42 45 47 50 524 - - - 16 19 53 57 60 63 665 - - 15 19 23 65 68 72 76 80
16 - 31 42 53 65 181 191 202 212 22217 - 32 45 57 68 191 202 213 224 23518 - 34 47 60 72 202 213 225 236 24819 - 36 50 63 76 212 224 236 248 26120 - 38 52 66 80 222 235 248 261 273
Using P Values.
If P </= reject the null hypothesis.If P > accept the null hypothesis.If P </= reject the null hypothesis.If P > accept the null hypothesis.
0.032< 0.05 Reject
Bone density between males & females over 50 is different
Example from LiteratureDrews (1995), BEHAVIOUR 133The pattern and context of injuries was studied in a troop of yellow baboons in Mikumi National Park (Tanzania).
TABLE 7. Median and range of healing times in days for baboon injuries.
N Median Min MaxTotal sample* 35 23.0 8 120Small injuries (<5cm) 15 14.0 8 114Large injuries (>5cm) 11 24.0 10 120* The sample is composed of 24 cuts, 4 punctures, 1 tear, 1 case of
limping, 1 bruise, and 3 injuries of unspecified shape.
Quote from results: The difference between small and large wounds (Table 7) was not statistically significant (Mann-Whitney test, Z= -1.461, N1=15, N2=11, p=0.14).
TABLE 7. Median and range of healing times in days for baboon injuries.
N Median Min MaxTotal sample* 35 23.0 8 120Small injuries (<5cm) 15 14.0 8 114Large injuries (>5cm) 11 24.0 10 120* The sample is composed of 24 cuts, 4 punctures, 1 tear, 1 case of
limping, 1 bruise, and 3 injuries of unspecified shape.
Quote from results: The difference between small and large wounds (Table 7) was not statistically significant (Mann-Whitney test, Z= -1.461, N1=15, N2=11, p=0.14).
What is Z? - bone example revisted.
Back to baboons….QUESTIONS:• What were the two samples? Sample
sizes?
• What might the raw data have looked like?
• Why might he have used a Mann-Whitney U test instead of a t-test?
Lecture Content
• Comparing t- & MWU tests
• t-test
• Mann Whitney U test