Key Concepts in Physiology Practicals
PHSI2X07/MEDS2001 –
Practicals Weeks
5-11 2024
Data interpretation and
presentation skills
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campuses stand. We pay our respects to those who have cared and continue to care for Country.
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Practical 1 (5%) 2% Kuracloud report submission to Canvas
3% Canvas Quiz (1 x SAQ and 4 x MCQ’s)
Practical 2 (5%) 2% Kuracloud report submission to Canvas
3% Canvas Quiz (1 x SAQ and 4 x MCQ’s)
Practical 3 (5%) 2% Kuracloud report submission to Canvas
3% Canvas Quiz (1 x SAQ and 4 x MCQ’s)
Practical 4 (5%) 2% Kuracloud report submission to Canvas
3% Canvas Quiz (1 x SAQ and 4 x MCQ’s)
Practical 5 (10%) 2% Kuracloud report submission to Canvas
8% Report (assess capacity to communicate results with graphs)
Assessment due 48 hours after Practical
Learning Objectives
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Data interpretation and presentation skills are
essential across multiple areas of research
– How to deal with different data sets and best standards for data
presentation
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What can you determine from these graphs?
Placebo Ozempic
0
10
20
30
40 Placebo Ozempic
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Placebo Ozempic
0
10
20
30
40
50
Effect of Ozempic on Body Mass Index (BMI)
BM
I
✱✱✱✱
What can you determine from these graphs?
Figure 1. Effect of Ozempic on body mass index (BMI) in
women 50-60 years of age treated for 12 months, Error bars ±
SEM, Student T-test **** p<0.0001
Figure 2. Effect of Ozempic on proportion of patients classified as being obese,
overweight, healthy or underweight in women 50-60 years of age treated for 12
months. Obese BMI ≥ 30.0, overweight BMI 25.0-29.9, healthy BMI 18.5-24.9,
underweight BMI ≤18.4. Two way ANOVA ** p<0.01, ****** p<0.0000001
Obese Overweight Obese
Healthy
Overweight
Underweight
Effect of Ozempic on Proportion of Obese and
Overweight Women
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Placebo
N=25
Ozempic
N=25
12%
88%
12%
24%
48%
**
16%
******(K
g/
m
2 )
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1) Define dependent and independent variables
Practical 1 - Endocrine
2) Determine appropriate graph type to communicate data
Practical 2 – Sensory & Motor
3) Produce a well labelled graph with title, x and y axes and figure legend
Practical 3 - Muscle
4) Perform appropriate statistics (T-test or One way ANOVA) and add error bars
Practical 4 - Cardiovascular
5) Produce a graph of practical data with all components covered in 1-4
Practical 5 – Respiratory – final report
Key Components of a High Quality Graph
Endocrine Practical
The independent variable is the one the experimenter controls.
The dependent variable is the variable that changes in response to the independent
variable.
If the independent variable changes, then the dependent variable is affected.
Dependent and Independent Variables
Today’s Practical Graphs
Independent Variable
Dependent
Variable
Today’s Practical Graphs
0
2
4
6
8
10
12
14
16
18
20
0 20 40 60 80 100 120 140 160 180 0
2
4
6
8
10
12
0 20 40 60 80 100 120
BL
oo
d
gl
uc
os
e
(m
m
ol
s/
L)
TIme (minutes)
Fasting Blood Glucose Response
Nerve Practical
Example data set – baby weight
36 Weeks 40 Weeks
3 4
2 5
4 3
3 4
Table 1: Weight of babies delivered at 36 and 40 Weeks
Average 3 4
Bar graphs with error bars (SEM)
P = 0.134 (t-test)
t-test will look at differences between 2 groups with a single independent variable
Overlayed scatter plot – shows raw data
P = 0.134 (t-test)
t-test will look at differences between 2 groups with a single independent variable
Example data set – baby weight
Female
32 Weeks
Female
36 Weeks
Female
40 Weeks
Male
32 Weeks
Male
36 Weeks
Male
40 Weeks
2 3 4 3 4 5
1.5 2 5 2 5 6
2.5 4 3 4 3 4
2 3 4 3 4 5
Table 3: Weight of male and female babies delivered at 32,
36 and 40 Weeks
Average 2 3 4 3 4 5
Two Way ANOVA will look at differences between multiple groups with a two distinct independent variables
Clustered Bar Graph
Two Way ANOVA will look at differences between multiple groups with a two distinct independent variables
Males are heavier than
females P = 0.005
Longer gestation results in
heavier babies P > 0.001
Example data set – baby weight
Birth 2 weeks 4 weeks 8 weeks
Name
Baby 1 3 2 3 4
Baby 2 4 3 4 5
Baby 3 2 1 2 3
Baby 4 3 2 3 4
Table 4: Weight of babies after birth
Average 3 2 3 4
Line graph
Age has an effect on body weight
P = 0.002 (RM ANOVA)
8-week-old babies are larger than 2-week-
old babies. P = 0.006 (Tukey test – RM
ANOVA on ranks (normality failed))
* P = 0.006
Muscle Practical
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– A brief title that accurately describes the content of the figure (Below the figure)
– A concise description of the content of the figure including a brief overview of the
methods used to obtain the presented data
– A summary of the results displayed in the figure and details of any statistical
analyses used to analyse the data
– Definition of any non-standard abbreviations used in the figure
– You do not need to include background literature related to the figure or a
discussion of the results presented
Figure Legends
Example figure and legend
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Fig. 7. Enhanced cochlear implant performance2 weeks after BDNF gene therapy in deafened
guinea pigs. Animals were deaf for 2 weeks before tandem configuration CFE, 5 pulses at 20 V. (A) eABR traces after
BDNF gene therapy or CFE using a gutted plasmid (GFP control). Red and blue arrowheads show current stimulus
needed to elicit a threshold response. Traces start after stimulus artifact. Representative full length (10 ms) traces for
the maximum stimulus levels are shown in the inset (S, stimulus artifact). (B) eABR thresholds of deafened guinea
pigs compared with the BDNF treatment (n = 5) and control groups, including GFP control vector (n = 5). The
reference group (Ref.) was implanted in normal-hearing guinea pigs (n = 5). The “Deaf 2wk” group was guinea pigs 2
weeks after chemical deafening (n = 10). Box plot solid lines show median, dashed lines show mean, and frame
defines 25th and 75th percentiles. Data are individual animal hearing thresholds. P values were determined by
ranked ANOVA with Holm-Sidak multiple pairwise comparisons. NS, not significant. (C) Individual eABR input-output
functions for BDNF-treated deafened guinea pigs versus GFP vector–treated deafened controls. Data show the
progressive growth in amplitude of the ABR p1-n1 wave as the stimulus current through the cochlear implant array is
increased. Different symbols indicate different animals (n = 5 for each group). (D) Average eABR input-output growth
functions from the data shown in (C) (means ± SEM). Linear best-fit trend lines are shown. P value was determined
by repeated measures two-way ANOVA for stimulus levels between 250 and 397 mA.
A brief title that accurately describes the content of the figure (Below the
figure)
Fig. 7. Enhanced cochlear implant performance2 weeks after BDNF gene therapy in deafened
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Animals were deaf for 2 weeks before tandem configuration CFE, 5 pulses at 20 Vguinea pigs. . (A) eABR traces after
BDNF gene therapy or CFE using a gutted plasmid (GFP control). Red and blue arrowheads show current stimulus
needed to elicit a threshold response. Traces start after stimulus artifact. Representative full length (10 ms) traces for
the maximum stimulus levels are shown in the inset (S, stimulus artifact). (B) eABR thresholds of deafened guinea
pigs compared with the BDNF treatment (n = 5) and control groups, including GFP control vector (n = 5). The
implanted in normal-hearing guinea pigsreference group (Ref.) was (n = 5). The “Deaf 2wk” group was guinea pigs 2
weeks after chemical deafening (n = 10). Box plot solid lines show median, dashed lines show mean, and frame
defines 25th and 75th percentiles. Data are individual animal hearing thresholds. P values were determined by
ranked ANOVA with Holm-Sidak multiple pairwise comparisons. NS, not significant. (C) Individual eABR input-output
functions for BDNF-treated deafened guinea pigs versus GFP vector–treated deafened controls. Data show the
progressive growth in amplitude of the ABR p1-n1 wave as the stimulus current through the cochlear implant array is
increased. Different symbols indicate different animals (n = 5 for each group). (D) Average eABR input-output growth
functions from the data shown in (C) (means ± SEM). Linear best-fit trend lines are shown. P value was determined
by repeated measures two-way ANOVA for stimulus levels between 250 and 397 mA.
A concise description of the content of the figure including a brief
overview of the methods used to obtain the presented data
Fig. 7. Enhanced cochlear implant performance2 weeks after BDNF gene therapy in deafened
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guinea pigs. Animals were deaf for 2 weeks before tandem configuration CFE, 5 pulses at 20 V. (A) eABR traces after
BDNF gene therapy or CFE using a gutted plasmid (GFP control). Red and blue arrowheads show current stimulus
needed to elicit a threshold response . Traces start after stimulus artifact. Representative full length (10 ms) traces for
the maximum stimulus levels are shown in the inset (S, stimulus artifact). (B) eABR thresholds of deafened guinea
pigs compared with the BDNF treatment (n = 5) and control groups, including GFP control vector (n = 5). The
reference group (Ref.) was implanted in normal-hearing guinea pigs (n = 5). The “Deaf 2wk” group was guinea pigs 2
Box plot solid lines show median, dashed lines show meanweeks after chemical deafening (n = 10). , and frame
defines 25th and 75th percentiles Data are individual animal hearing thresholds. . P values were determined by
ranked ANOVA with Holm-Sidak multiple pairwise comparisons . NS, not significant. (C) Individual eABR input-output
functions for BDNF-treated deafened guinea pigs versus GFP vector–treated deafened controls. Data show the
progressive growth in amplitude of the ABR p1-n1 wave as the stimulus current through the cochlear implant array is
Different symbols indicate different animalsincreased. (n = 5 for each group). (D) Average eABR input-output growth
functions from the data shown in (C) (means ± SEM). Linear best-fit trend lines are shown. P value was determined
by repeated measures two-way ANOVA for stimulus levels between 250 and 397 mA.
A summary of the results displayed in the figure and details of any
statistical analyses used to analyse the data
Fig. 7. Enhanced cochlear implant performance2 weeks after
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BDNF gene therapy in deafened
eABRguinea pigs. Animals were deaf for 2 weeks before tandem configuration CFE, 5 pulses at 20 V. (A) traces after
BDNF gene therapy or CFE using a gutted plasmid (GFP control). Red and blue arrowheads show current stimulus
needed to elicit a threshold response. Traces start after stimulus artifact. Representative full length (10 ms) traces for
the maximum stimulus levels are shown in the inset (S, stimulus artifact). (B) eABR thresholds of deafened guinea
pigs compared with the BDNF treatment (n = 5) and control groups, including GFP control vector (n = 5). The
reference group (Ref.) was implanted in normal-hearing guinea pigs (n = 5). The “Deaf 2wk” group was guinea pigs 2
weeks after chemical deafening (n = 10). Box plot solid lines show median, dashed lines show mean, and frame
defines 25th and 75th percentiles. Data are individual animal hearing thresholds. P values were determined by
ranked ANOVA with Holm-Sidak multiple pairwise comparisons. NS, not significant. (C) Individual eABR input-output
functions for BDNF-treated deafened guinea pigs versus GFP vector–treated deafened controls. Data show the
progressive growth in amplitude of the ABR p1-n1 wave as the stimulus current through the cochlear implant array is
increased. Different symbols indicate different animals (n = 5 for each group). (D) Average eABR input-output growth
functions from the data shown in (C) (means ± SEM). Linear best-fit trend lines are shown. P value was determined
by repeated measures two-way ANOVA for stimulus levels between 250 and 397 mA. eABR; Electrically-evoked
auditory brainstem response, CFE; Close field electroporation, BDNF; Brain derived neurotrophic factor.
Definition of any non-standard abbreviations used in the figure
Cardiovascular Practical
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What type of Statistical Analysis to do?
– Today’s data: Comparing HR after Dynamic vs Static exercise
– Comparing two groups of data – Student’s T-Test
– The assumptions are:
1. The data follow a normal distribution
2. The two data sets come from distributions that may differ in their mean value, but
not in the standard deviation
3. The observations are independent of each other
What type of Statistical Analysis to do?
– Today’s data: Comparing HR after Dynamic vs Static exercise
– Comparing two groups of data – Student’s T-Test
– The assumptions are:
1. The data follow a normal distribution
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What type of Statistical Analysis to do?
– Today’s data: Comparing HR after Dynamic vs Static exercise
– Comparing two groups of data – Student’s T-Test
– The assumptions are:
2. The two data sets come from distributions that may differ in their mean value, but
not in the standard deviation
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What type of Statistical Analysis to do?
– Today’s data: Comparing HR after Dynamic vs Static exercise
– Comparing two groups of data – Student’s T-Test
– The assumptions are:
3. The observations are independent of each other
• In general, repeated measurements on the same individual are not independent.
• If we had 20 leg ulcers on 15 patients, then we have only 15 independent observations.
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How to do a T-Test in Excel
1. Get averages from 5 extra groups
2. Tabulate your data
– Baseline and Exercise for Dynamic and Static groups:
Baseline
Dynamic Static
82 64
62 82
80 76
71 73
78 76
80 76
Exercise
Dynamic Static
138 104
120 108
120 96
127 112
122 96
149 122
How to do a T-Test in Excel
3. Calculate Significance using a T-Test
– Excel formula: =T.TEST(array1,array2,tails,type)
– The T.TEST function syntax has the following arguments:
• Array1: The first data set.
• Array2: The second data set.
• Tails: Specifies the number of distribution tails.
1. If tails = 1, T.TEST uses the one-tailed distribution.
2. If tails = 2, T.TEST uses the two-tailed distribution.
• Type: The kind of t-Test to perform.
1. Paired
2. Two-sample equal variance (homoscedastic)
3. Two-sample unequal variance (heteroscedastic)
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Interpreting your T-Test
– What does your statistics test mean?
– P = 0.8051
– (> 0.05)
– Non-Significant
– ‘ns’ goes on the graph and figure legend
– P = 0.0045
– (round to the first single digit)
– P<0.01
– ‘**’ goes on the graph and put what the * mean in the legend
– Note: Each * is more stringent P values: * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001…
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Plotting Graphs to show Statistics
– Calculate Mean and SD from raw data
– Plot a graph
– (don’t forget axis labels and units)
– Add the significance
– Line between the bars you are comparing
– Significance ‘value’ (previous slide)
• * or ns etc
• (do as text box and shape)
– Screenshot of graph goes into Kuracloud
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Figure Legend with Statistical Information
Figure 1. Effect of different exercise types (dynamic and static) on
heart rate (bpm) in healthy volunteers. Data are mean (n=6), error
bars are SD, Student’s T-Test, non-significant (ns), ** P<0.01.
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