How to give a memorable presentation Examples and tips for an effective presentation

Dr Jenny Freeman, University of Sheffield

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Starting out• Knowledge

• Material

Outline

• Before the day

• On the day

Outline

•Before the day• On the day

Do your homework

• Audience

The model:

Where:

b0, b1, b2, b3, b4, b5 are constants

sin(ωt) and cos(ωt) allow for seasonality

ωt=(2πt)/12

b3 is the slope

b4 allows for change in slope at t0 where t0 is the time of the intervention

t’ = 0 when t< t0

t’ = t- t0 when t≥ t0

δ(t) allows for break in curve (interruption)

δ(t)) = 0 when t< t0

δ(t)) = 1 when t≥ t0

ξt is the error term which we allow to have first order autocorrelation structure with parameter ρ

yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt

Do your homework

• Audience

• Venue

Do your homework

• Audience

• Venue

• Timing

Before the day

• Message

• Slide design

Before the day

• Message

• Slide design

• Practise

Before the day

• Message

• Slide design

• Practise

• Practise

Before the day

• Message

• Slide design

• Practise

• Practise

•Practise again

Practise• NEVER, ever don’t practise

• Do it at least twice (if possible)

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Voice trainingUsed under a Creative Commons By Attribution Licence -Some rights reserved by MiiiSH

Slides

Graphic design of slides

• Key elements−Text

−Pictures and graphics

−Colour

−Space

Graphic design of slides: text

Slide design

• Keep it simple

• Keep it legible

• Generally, light text on a darker background projects well

• Sans serif fonts such as Arial are more easily read when projected.

Slide design

• Keep it simple

• Text is meant to be read, so ensure that slides are legible

• No hard and fast rules, but in general, light text on a darker background projects well

• Sans serif fonts such as Arial are more easily read when projected.

What do you think?

Graphic design of slides: pictures, graphics & animations

For example….

And what do the students think?

• small group sessions

• lectures and lecture notes

• the videos

• first two lectures

• clinical examples

• logical and clear to understand

What did the students find most useful:

What did the students find least useful:

• the group sessions

• the lectures as they are too hard

• the videos

• the early sessions

• clinical scenarios

• too much stats

And what do the students think?

• small group sessions

• lectures and lecture notes

• the videos

• first two lectures

• clinical examples

• logical and clear to understand

What did the students find most useful:

What did the students find least useful:

• the group sessions

• the lectures as they are too hard

• the videos

• the early sessions

• clinical scenarios

• too much stats

Graphic design of slides: colour

Graphic design of slides: space

For example….

20/04/23 © The University of Sheffield

What do we mean when we talk about bivariate data• Data where there are two variables

• The two variables can be either categorical or numerical

• This session we are dealing with continuous bivariate data i.e. Both variables are continuous

• We have also looked at categorical bivariate data.....

..... Categorical bivariate data from the risk lecture:

• There are two binary (categorical) variables Type of statin (Baycol/other)

Whether died of rhabdomylysis

• With these data we examined the risk of death from rhabdomyolysis of Baycol compared to other statins

Baycol Other statins

Number who die from rhabdomyolysis

2 1

Number who are alive or died from other causes

999,998 9,999,999

Total 1,000,000 10,000,000

What do you think now…?

What do we mean when we talk about bivariate data

• Data where there are two variables

• The two variables can be either categorical or numerical

• This session we are dealing with continuous bivariate data i.e. Both variables are continuous

• We have also looked at categorical bivariate data...

...categorical bivariate data example from Risk lecture

• There are two binary (categorical) variables Type of statin (Baycol/other)

Whether died of rhabdomylysis

• With these data we examined the risk of death from rhabdomyolysis of Baycol compared to other statins

Baycol Other statins

Number who die from rhabdomyolysis

2 1

Number who are alive or died from other causes

999,998 9,999,999

Total 1,000,000 10,000,000

Highlighting key points

The model:

Where:

b0, b1, b2, b3, b4, b5 are constants

sin(ωt) and cos(ωt) allow for seasonality

ωt=(2πt)/12

b3 is the slope

b4 allows for change in slope at t0 where t0 is the time of the intervention

t’ = 0 when t< t0

t’ = t- t0 when t≥ t0

δ(t) allows for break in curve (interruption)

δ(t)) = 0 when t< t0

δ(t)) = 1 when t≥ t0

ξt is the error term which we allow to have first order autocorrelation structure with parameter ρ

yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt

The model:

Where:

b0, b1, b2, b3, b4, b5 are constants

yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt

The model:

Where:

b0, b1, b2, b3, b4, b5 are constants

sin(ωt) and cos(ωt) allow for seasonality

ωt=(2πt)/12

yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt

The model:

Where:

b0, b1, b2, b3, b4, b5 are constants

sin(ωt) and cos(ωt) allow for seasonality

ωt=(2πt)/12

b3 is the slope

yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt

The model:

Where:

b0, b1, b2, b3, b4, b5 are constants

sin(ωt) and cos(ωt) allow for seasonality

ωt=(2πt)/12

b3 is the slope

b4 allows for change in slope at t0 where t0 is the time of the intervention

t’ = 0 when t< t0

t’ = t- t0 when t≥ t0

yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt

The model:

Where:

b0, b1, b2, b3, b4, b5 are constants

sin(ωt) and cos(ωt) allow for seasonality

ωt=(2πt)/12

b3 is the slope

b4 allows for change in slope at t0 where t0 is the time of the intervention

t’ = 0 when t< t0

t’ = t- t0 when t≥ t0

δ(t) allows for break in curve (interruption)

δ(t)) = 0 when t< t0

δ(t)) = 1 when t≥ t0

yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt

The model:

Where:

b0, b1, b2, b3, b4, b5 are constants

sin(ωt) and cos(ωt) allow for seasonality

ωt=(2πt)/12

b3 is the slope

b4 allows for change in slope at t0 where t0 is the time of the intervention

t’ = 0 when t< t0

t’ = t- t0 when t≥ t0

δ(t) allows for break in curve (interruption)

δ(t)) = 0 when t< t0

δ(t)) = 1 when t≥ t0

ξt is the error term which we allow to have first order autocorrelation structure with parameter ρ

yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt

Potential models: No impact at all

yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt

Potential models: Change in slope

yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt

Potential models: Break in curve

yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt

Potential models: Both change in slope and break

yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt

Tables in presentations

• What do you think?

Baseline characteristics(n=584,321)

Missing data(%)

Before Minor Injuries

(Dec 2002)

Before 90% target

(March 2004)

Before 98% target

(January 2005)

After 98% target

N 250,489(7,828/month)

122,938(8,196/

month)

87,647(8,765/month)

123,247(8,803/

month)

Gender: % Male (0.0%) 54.3 54.0 53.9 53.9

Age: median (IQR)

(0.1%) 40.0(27.2 to 61.5)

40.9(27.1 to 62.8)

40.8(26.8 to 61.4)

40.4(26.3 to

60.6)

% > 65 years (0.1%) 22.0 23.2 22.1 21.2

% with trauma (1.6%) 52.3 50.7 50.4 49.1

% by ambulance - 33.4 33.8 31.3 31.8

% seen & treated within 4 hours

(0.3%) 82.9 77.8 82.8 88.2

Tables in presentations

• Can we do better?

Baseline characteristics(n=584,321)

Missing data(%)

Before Minor Injuries

(Dec 2002)

Before 90% target

(March 2004)

Before 98% target

(January 2005)

After 98% target

N

Gender: % Male (0.0%)

250,489(7,828/month)

54.3

122,938(8,196/

month)

54.0

87,647(8,765/month)

53.9

123,247(8,803/

month)

53.9

Baseline characteristics(n=584,321)

Missing data(%)

Before Minor Injuries

(Dec 2002)

Before 90% target

(March 2004)

Before 98% target

(January 2005)

After 98% target

N

Gender: % Male

Age: median (IQR)

(0.0%)

(0.1%)

250,489(7,828/month)

54.3

40.0(27.2 to

61.5)

122,938(8,196/

month)

54.0

40.9(27.1 to

62.8)

87,647(8,765/month)

53.9

40.8(26.8 to

61.4)

123,247(8,803/

month)

53.9

40.4(26.3 to

60.6)

Baseline characteristics(n=584,321)

Missing data(%)

Before Minor Injuries

(Dec 2002)

Before 90% target

(March 2004)

Before 98% target

(January 2005)

After 98% target

N

Gender: % Male

Age: median (IQR)

% > 65 years

(0.0%)

(0.1%)

(0.1%)

250,489(7,828/month)

54.3

40.0(27.2 to 61.5)

22.0

122,938(8,196/

month)

54.0

40.9(27.1 to 62.8)

23.2

87,647(8,765/month)

53.9

40.8(26.8 to 61.4)

22.1

123,247(8,803/

month)

53.9

40.4(26.3 to

60.6)

21.2

Baseline characteristics(n=584,321)

Missing data(%)

Before Minor Injuries

(Dec 2002)

Before 90% target

(March 2004)

Before 98% target

(January 2005)

After 98% target

N

Gender: % Male

Age: median (IQR)

% > 65 years

% with trauma

(0.0%)

(0.1%)

(0.1%)

(1.6%)

250,489(7,828/month)

54.3

40.0(27.2 to 61.5)

22.0

52.3

122,938(8,196/

month)

54.0

40.9(27.1 to 62.8)

23.2

50.7

87,647(8,765/month)

53.9

40.8(26.8 to 61.4)

22.1

50.4

123,247(8,803/

month)

53.9

40.4(26.3 to

60.6)

21.2

49.1

Baseline characteristics(n=584,321)

Missing data(%)

Before Minor Injuries

(Dec 2002)

Before 90% target

(March 2004)

Before 98% target

(January 2005)

After 98% target

N

Gender: % Male

Age: median (IQR)

% > 65 years

% with trauma

% by ambulance

(0.0%)

(0.1%)

(0.1%)

(1.6%)

-

250,489(7,828/month)

54.3

40.0(27.2 to 61.5)

22.0

52.3

33.4

122,938(8,196/

month)

54.0

40.9(27.1 to 62.8)

23.2

50.7

33.8

87,647(8,765/month)

53.9

40.8(26.8 to 61.4)

22.1

50.4

31.3

123,247(8,803/

month)

53.9

40.4(26.3 to

60.6)

21.2

49.1

31.8

Baseline characteristics(n=584,321)

Missing data(%)

Before Minor Injuries

(Dec 2002)

Before 90% target

(March 2004)

Before 98% target

(January 2005)

After 98% target

N

Gender: % Male

Age: median (IQR)

% > 65 years

% with trauma

% by ambulance

% seen & treated within 4 hours

(0.0%)

(0.1%)

(0.1%)

(1.6%)

-

(0.3%)

250,489(7,828/month)

54.3

40.0(27.2 to 61.5)

22.0

52.3

33.4

82.9

122,938(8,196/

month)

54.0

40.9(27.1 to 62.8)

23.2

50.7

33.8

77.8

87,647(8,765/month)

53.9

40.8(26.8 to 61.4)

22.1

50.4

31.3

82.8

123,247(8,803/

month)

53.9

40.4(26.3 to

60.6)

21.2

49.1

31.8

88.2

Life after death by Powerpoint• http://www.youtube.com/watch?

v=lpvgfmEU2Ck

Powerpoint summary

Powerpoint summary

• Keep slides simple

• Keep slides consistent

• Make sure they are legible

• Graphs easier to read than tables

• In general six words per row, six lines per slide

• Use graphics and animations sparingly

On the day

On the day

• Breathe

On the day

• Breathe

• Friend

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First impressions

• Dress

• Eye contact

Props/crutches

20/04/23 © The University of Sheffield

20/04/23 © The University of Sheffield

How to stop

• Summary

• Main message

• Your contact detailsUsed under a Creative Commons By Attribution Licence -Some rights reserved by bpende

Dealing with questions

• Always repeat the question

Dealing with questions

• What do you do if you don’t know the answer?

Learning from others

• Poor speakers

• Good speakers

What will you do differently next time?

What will you do differently next time?• Do your research

• Prepare well in advance

• Practise again, and again, and again

• Plant a friend in the audience

• Breathe (& smile)

j.v.freeman@shef.ac.uk

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