You Have 10 Seconds. Go.

10 seconds - that’s all that it takes for the audience to judge your presentation.

We all have been there. It was a nice cozy noon. The lunchtime was near, and we were invited (i.e., asked) to attend a presentation. Five minutes into the presentation, immediately after the introduction slide, we were introduced to a wall of text, and another, and another, and a massive diagram with heaps of tiny texts and numbers, and then another wall of text, and another. And before we know it, we were in a difference plane of existence between “awaken” and “sleeping”. No amount of coffee could bring us back at that point. Finally, lunch time came. We were relieved, before we realized that we were the next ones to present.

What should we do? Certainly, we do not want to bring our audience into another plane of existence during our presentation. It doesn’t have to be this way. After all, we have also witnessed amazing talks that changed and inspired the world throughout the history. So, how can we up our game?

Recently, I attended a workshop on presentation skills for research students and researchers by Mrs. Sharon Ferrier, under the CARST program of The University of Adelaide. It was a fantastic workshop with many good points. In this post, I am going to sharing some interesting content that I learned from the workshop.

Preparing a Good Presentation

For many of us, presentation is simply something that we have to do: We have a conference paper accepted, we have a scheduled reading group meeting, or we have a seminar that we are assigned. However, let’s look beyond the obligation to see what is the “point” of giving these talks?

Essentially, we “sell” something in any presentation. It can be an idea, it can be a research paper, it can be a project proposal, it can be new product. We pitch to sell something in these presentations. And when we realize that we are trying to make people “buy” something from us, suddenly, presentations do not seem to be so vain anymore.

Convincing presentations, according to Mrs. Ferrier, share one common characteristic: they make the audience Feel something to urge them to Take an Action. Essentially, if you can make your audience feel good enough (or scared enough), they might “buy” your stuff.

So, how can we build such presentation? Mindful preparation and a lot of practice. The process of preparing a presentation can be divided into three parts:

1. Prepare the Content
2. Prepare the Slides and Notes
3. Prepare the Delivery Techniques

Preparing Content

When it comes to content, Clarity is the King. We achieve clarity through focusing on the content overlapping between our objectives and the needs of our audience; and through cutting everything else from our presentation ruthlessly. For instance, if we are trying to convince our customers to decorate their garden with our roses, we have to focus on the simplicity of growing our roses, instead of the rose-shaped chocolate pie that we bought yesterday (even though it is really tempting).

Objective and Audience

To decide the objective of our communication, we have to be brutally honest with ourselves. To know our audience, we have to do some investigation, through asking the event organizer or coming earlier to have a talk with our potential audience. Different audience requires different types of vocabulary and content organization. For instance, task-oriented, introvert audience usually enjoy facts and details instead of constant interactions. A people-oriented, extroverted audience, on the other hand, requires the opposite. Similarly, the content for people knowing under-the-hood of the topic must be different from the content for people having only superficial “satellite-view” of the topic. A good strategy is starting with big headlines and then moving to the minute details.

Building the Contents

With the objective and audience decided, we can start building the content of the presentation. The first step is brainstorming with mindmap to draw out all ideas related to the topic. Just keep going until you cannot continue. It might take a while. As a demonstration, I included the mindmap of this blog post.

After the mindmap is created, the next step is filering with respect to our predefined objectives and audience. Clarity is the king. Anything that does not lie in the interaction between audiences and our objective must be cut out ruthlessly.

Building the Outline

With the filtered content in hand, the task of building an outline is trivial. The only issue here is building a 10-second grab. As stated previously, we only have 10 seconds to convince our audience that our presentation worths listening to. So, how can we open our presentation with a bang that hook the audience in the first 10 seconds? We have to be creative and precise.

1. Introduction
   . 10s grab
   . Significance of the topic
2. Body
   . Topic 1
   . Topic 2
   . Topic 3
3. Conclusion

Preparing Slides and Notes

The Slides (e.g., a Power Point file) is a visual tool. It can aid, or break a presentation. In fact, the research of Prof. John Sweller showed that showing text on a slide, even in small amount, while talking actually reduces the retention of the audience. Therefore, it is crucial to design effective slides. This can be done by:

1. Reducing the amount of text. Use graphs and photos instead, unless it is absolutely impossible. Using “Smart Arts” can actually increase the slides-making process while increasing the clarity of the slides.
2. Reducing the number of slides.

Again, the use of visual aids must also be filtered with the objectives and the audience of the presentation. In short, do not use the image of an elephant jumping around while the main text drops letter by letter simply because “I can”.

The nodes that accompany the slides is a useful tool that is commonly misused. The worst way possible to use notes is storing the whole script and read it word-by-word in the conference. Instead, the notes should be used to remind us of the outline of the presentation. They should be printed in large font so that they can be seen easily when placed on the table away from us. A good set of notes help us to focus more on the audience while staying with the script.

Preparing the Delivery

The purpose of all stated preparations is to build our confidence for the actual presentation. An effective presentation depends on the way we deliver the content as much as the content itself. The ultimate goal of the presentation is to “sell” our message. This goal can be achieved with a set of techniques.

First, 10-second grab. As we stated previously, it takes audience only 10 seconds in the beginning to judge a presentation. To deliver our message effectively, we need to grab the attention of audience immediately in the beginning with big headlines and buzzwords to cause emotional response. Emotional response is our target. If we cannot stir-up our audience, it is unlikely that our message can reach them. We can cause emotional responses by targeting needs of our audience. Logic (e.g., facts) is also a powerful tools, especially in technical presentation. After all, a presentation without any useful information is a waste of time, and most of us hate wasting time. Finally, any exciting presentation must conclude with a call for action, else, all of our efforts would lead to minimal, if any, concrete results.

Performing these techniques requires confidence and familarity with presentation that can only be achieved through exposure. In other word, the more presentations we do, and the more feedbacks we receive, the more skillful we becomes. Feedback is the key to improvement, especially something that are invisible to us such as our gesture, facial expression and posture.

The Biggest Secret

Even after all of the stated preparations, there is still a secret left.

That is practice, practice and even more practice.

Steve Jobs, a master in presentation, is well-known for his intense preparation. He starts to prepare months before the actual event. Another example is Dr. Jill Bolte-Taylor, a popular speaker in TED events. She rehearses [200 times] before a presentation.

As I stated previously, feedback is crucial for improvement. A good to to get feedbacks from our rehearsal is through video recording. Through the process of recording, evaluating and fine-tuning, we will gradually approach an effective presentation that can be delivered effortlessly.

Monty Hall Problem - When Perception Does Not Align with Reality

This post is a part in my series on Statistics and Probability topics from the book Think Stats of Professor Allen B. Downey.

I admit. I have a weird relationship with Statistics and Probability. By weird, I mean that I love to get to know them, and they love to be strangers. Now, I will not blame any anything or anyone for my knowledge gap. That being said, I must say that an appropriate approach to this interesting and important subject will take us much further with much less resistance.

On my journey to find this approach, I came across the interesting book “Think Stats” of Prof. Allen B. Downey, Franklin W. Olin College of Engineering. The hypothesis of this book is that programming can be an excellent vessel to bring a person into the world of Statistics and Probability. It cannot replace a formal Statistics course, but it will make this formal course much more accessible. And more important, it makes Statistics tangible. I will write an article about this book in near future. Today, I will discuss about an interesting problem presented in this book that makes me reconsider the way we perceive the world.

Monty Hall Problem

Monty Hall problem is a probability puzzle based on an American game show called “Let’s Make a Deal”. This puzzle is presented in chapter 5 of Think Stats. It can be summarized as follow:

The goal of the game is selecting the door with the hidden car. The player is presented with 3 doors. The car is hidden behind one door, while junks are hidden behind the remaining ones. After the player selecting a door, Monty Hall (i.e., the host of the game show) opens one of the remaining door, which always does not have the car. Now, the player has to choice either to switch or to stay with the selected door. Afterward, Monty Hall opens the remaining game and reveals the final result.

The question of the Monty Hall Problem is whether the player should switch, or stay with the selected door.

Perception

It is simple. We have two choices: our selelected door, and the remaining door. The car must be behind one of these two doors, so each door has 50% chance of having the car. Therefore, the decision to stay or to switch is meaningless.

When we are presented with these choices, I think we also “feel” that behind every close door, their is either a wonderful new sport car, or a pile of junks, so the chance of having a car when opening each door is 50%. And, therefore, the decision to stay or to switch is meaningless.

These views are natural and straightforward. Are they correct?

Monte Carlo Simulation

Instead of getting into an endless debate, let’s have a look at a simulation first. As suggested by the book, I wrote a simple simulation the game in Python. In this simulation, the car is placed randomly behind a door, picked from a uniform distribution. Without losing generality, I let the player pick door 1. Depending on the actual position of the car, Monty Hall opens a door, which is always empty. Depending on the given parameter into the simulation, the player can either stick with door 1 or switch to the remaining door. A boolean value is returned by the end of the simulation, reflecting the result of the game.

def MontyHall(switch, startDoor, verbose = False):
    """Play a Monty Hall game.
    Args:
        switch = indicate the decision to switch to the other gate
        startgate = the indicator of the selected gate of a user
    Returns:
        True/False = Win/Lose
    """
#    Initialize the game and put the car behind a random door.
    carDoor = random.randrange(0,3)
    selectedDoor = startDoor
    remainingDoors = [i for i in [0,1,2] if i != selectedDoor]
    
    if verbose:
        print("Start Door:%s - Car Door:%s - Switch:%s - Remaining Doors:%s" % (startDoor, carDoor, switch, remainingDoors))

    if startDoor < 0 or startDoor > 2: return None
    if selectedDoor == carDoor:
        if switch == True: selectedDoor = remainingDoors[random.randrange(0,2)]
    else:
        if switch == True: selectedDoor = carDoor
    
    if selectedDoor == carDoor:
        if verbose:
            print("selectedDoor:%s => Won!" % selectedDoor)
        return True
    else:
        if verbose:
            print("selectedDoor:%s => Lose!" % selectedDoor)
        return False

The simulation is repeated a large number of time to estimate the winning probability of staying and switching.

def MontyHallSim(startDoor, verbose = False, ite = 10000):
    winProbNoSwitch = 0.0
    winProbSwitch = 0.0
    win = 0
    for i in range(0,ite):
        if MontyHall(False, startDoor, verbose):
            win += 1
    winProbNoSwitch = win / float(ite)
    print("No Switch: Win:%s - Prob:%s" % (win, winProbNoSwitch))
    win = 0
    for i in range(0,ite):
        if MontyHall(True, startDoor, verbose):
            win += 1
    winProbSwitch = win / float(ite)
    print("Switch: Win:%s - Prob:%s" % (win, winProbSwitch))
    print("Relative risk: switch/no_switch = %s" % (winProbSwitch/winProbNoSwitch))

And here is the result:

After 10000 iterations:
No Switch: Win:3381 - Prob:0.3381
Switch: Win:6568 - Prob:0.6568
Relative risk: switch/no_switch = 1.94262052647

Surprisingly, we are twice more likely to win the car if we switch to the remaining door.

Why?

Analytical Solution

There was nothing wrong with the simulation, as far as I am aware. Nor there is anything wrong with the conclusion. Repeated simulations are independent from each other, and their results are identically distributed. Therefore, from a frequentism point of view, the resulting propability is perfectly valid.

In fact, using from a Bayesian point of view, we can confirm the simulation result analytically. Let’s denote three doors of the game as A, B C, and let be the events that the car is behind door A, B, or C, respectively. Assume that the player selects door A at the beginning, and Monty Hall opens door B. Let be the event that Monty Hall opens door B. We want to compare the probability of having the car behind the door A and the door C, given the evidence that Monty Hall opened door B.

Probability of having the car behind door C, given the evidence from Monty Hall is calculated as follow:

Probability of having the car behind door A, given the evidence from Monty Hall is:

Probability of having a car behind each door, prior to any evidence is 1/3. Also, because there are always two doors left, the probability of having Monty Hall to open any of two doors, without considering the actual location of the car is 1/2. The tricky part is the likelihood (i.e., probability that Monty Hall opens a door if he knows the location of the car). If the car is behind door C, Monty Hall has to open door B, so the probability is 1. On the other hand, if the car is behind door A, Monty Hall can pick the remaining doors at random, so this probability is 1/2.

As a result, because Monty Hall always know the exact position of the car, the probability of having car of each door changes after he opens one of them. It should be noted that this result is only valid if Monty Hall knows the exact position of the car. Otherwise, .

Another explanation from Numberphile is also interesting. It can be seen that in the beginning, the probability of each door having the car is 1/3. However, after the door B is eliminated, its probability is “pushed” to the remaining door C. In order word, door C now holds the probability of two doors combined.

Implication

Despite the confirmation of both analysis and simulation, I admit that it is still difficult to agree with this valid solution. No matter what the fancy mathematics say, when facing a closed door with a serial killer (possibly) standing behind it, my mind will always tell me that the chance of dying is 50%. I guess our perception is much less accurate than our expectation. Therefore, whenever I see a pattern, a cluster, or I am going to make a remark that an event is “likely” or “impossible”, I guess I should ask statistics first.

But then, again, there are “lies, dammed lies, and statistics”.