Guest Post: Risk, Insurance and the Actuary

Risk, Insurance, and the Actuary

Risk is an inherent part of our daily life. As a result, most of us, take out insurance policies as a means of protection against scenarios which, were they to occur, may cause hardship whether for us or, as in the case of life insurance, for our families.

Insurance companies write many types of policies. The mutual risks of the policy holders are shared so that claims made against the policies can be covered at a much reduced cost. If priced fairly, then the premium reflects the contribution of the insured’s risk to overall risk.

As policy holders – we want the best price to cover the risk we are offloading; shareholders (again often us if we have superannuation)of the insurance company –require the premiums be sufficient to ensure the company stays in business.

It is then very important that analysts pricing the policies (and those calculating the required level of capital to meet the claim liabilities) have the statistical knowledge necessary to measure risk accurately! Understanding risk is even more critical in the framework of Solvency II (*) capital requirements (if it ever gets enforced).

The task is made more difficult as the duration of the policy life varies considerably. Some insurance cover is claimed against shortly after the incident occurs with a short processing time – automobile accidents for instance typically fit this category. This class of cover is termed short-tail liabilities as payments are completed within a short timeframe of the incident occurring.

Other cases arise many years after the original policy was taken out, or payments may occur many years after the original claim was raised – for example medical malpractice. These are termed long-tail liabilities as payments may be made long after the original policy was activated or the incident occurred. Due to the long forecast horizon and [generally] higher volatility in the claim amounts, long-tail liabilities are inherently more risky.

Life insurance is in its own category as everybody dies sometime.

Meet the data

For convenience, and because it is generally less well understood, we restrict our focus to long-tail liability insurance data

For each claim we have many attributes, but four that are universal to all claims: payment amount(s), incident date (when the originating event resulting in the claim occurred), payment date(s), and state of claim (are further payments possible or is the claim settled). These attributes allow the aggregation of the individual claim data into a series more amenable for analysis at the financial statement level where the volatility of individual claims should be largely eliminated since the risk is pooled.

Actuaries tend to present their data cumulatively in a table like this:

Actuarial tableWhere the rows are accident years, and the column index (development time in actuarial parlance) is the delay between the accident year and the year of payment.

Thus payments made in development lag 0 corresponds to all payments made toward claims in the year the accident occurred. The values in development lag 10 correspond to the sum of the payments made in the eleven years since the accident occurred.

This presentation likely arose for a number of reasons, but the most important two being:

  • Cumulative data are much easier to work with in the absence of computers;
  • Volatility is visibly less of an issue the further in the development tail when examining cumulatives.

The nature of the inherited data presentation produces some unfortunate consequences:

  • Variability is hard to quantify between parameter uncertainty and process volatility;
  • Calendar year effects (trends down the diagonals) are unable to be measured – and therefore readily predicted;
  • Parameter interpretation is difficult due to the calendar year confounding effects; and
  • Parsimony is hard to achieve.

The actuarial profession attempts to deal with each of these issues in various ways. For instance, the bootstrap is being used to quantify variability. Data may be indexed against inflation to partially account for calendar year trends.

Why spend time on this?

Fundamentally because, if you want to solve a problem, you first have to be sure that the data you are using and the way you are using it allows you to solve the problem! The profession has spent much time, energy, and analysis on developing techniques to solve the risk measurement problem but with the underlying assumption that cumulation is the way to analyse insurance data.

Aside: this is why I enjoy Genetic Programming – not because the algorithm allows the automatic generation of solutions, but rather because you have to formulate the problem very precisely in order to ensure the right problem is solved.

Understanding the problem

The objective of analysis of the Insurance portfolios is to quantify the expected losses incurred by the Insurance company and the volatility (the risk) associated with the portfolio so adequate money is raised to pay all liabilities, at a reasonable price, with an excellent profit. Additional benefits may arise like an improved understanding of the policies being written, targeting of more profitable customers, and so forth, but these are secondary.

Assume the data available are the loss data with the three attributes of accident time, calendar time, and payment. Forget about claim state for now though this is an important factor for future projections.

We immediately identify two time attributes. This suggests time series models are likely a good starting point for analysis. We also would examine the distribution(s) of incremental losses rather than cumulate the losses over time since cumulation of time series would hide the volatility of the losses at the individual time points – the very component that we are interested in.

Further, we need the ability to distinguish between parameters, parameter uncertainty, and the process volatility. Process volatility and parameter uncertainty drive the critical risk metrics which are essential to ensuring adequate capital is set aside to not only cover the expected losses, but also allow for the unexpected losses should they occur.

Beginning with this foundation, modelling techniques which take the fundamental time-series nature of the data into account are almost certain to provide superior performance to methodologies which mask (for historical reasons mentioned) the time series nature of the data.

Is this new?

Actually, no. All the above considerations of analysis of P&C insurance data were presented many years ago. However, time series approaches are not typically taught to aspiring P&C actuaries. Why?

Perhaps several reasons:

  • Tradition. Like any specialised profession, a system is developed to provide solutions and unless the system is convincingly broken, the uptake of new methodology is resisted.
  • Statistical analysis is complicated. Applying standard formula to get answers is “easy” when you know the formula.

The catch

Misrepresenting data leads to a flawed model representing the underlying data processes.

The likelihood of such a methodology resulting in the correct mean or a correct measure of the volatility is extremely low. The distributional assumptions are likely completely spurious as the fundamental nature of the data is not recognised.

Wrong model = wrong conclusion, unless you’re unlucky

It is often a general problem where the wrong statistical technique is applied to solve a statistical problem. This suggestion the statement: “All models are wrong, but some are useful.” This is not entirely fair in my mind as it (wrongly) places the blame on the model where the blame should actually be on the analyst and their choice of the modelling method.

Although we will never find the model driving the underlying data generating process, nevertheless, we can often well approximate the data process (otherwise modelling of any kind would be pointless). These are the useful models. Then you are only unlucky if your model looks like it is useful, but fails when it comes to prediction.

In summary

  • The problem of quantifying risk is not a simple exercise
  • Insurance data is fundamentally financial time series data
  • The right starting point is critical to any statistical analysis
  • We statisticians need to explain our solutions in a way that is meaningful to established professions

(*) In essence, Solvency II comprises insurance legislation aiming to improve policyholder protection by introducing a clear, comprehensive framework for a market consistent, risk model. In particular, insurance companies must be able to withstand a 1/200 year loss event in the next calendar year encompassing all levels of risk sources – insurance and reserve risk, catastrophe risk, operational risk, default risk to name a few.  Quantitative impact study documents are available here; a general discussion of Solvency II can be found here. The legislation has been postponed many times.

About David Munroe

David Munroe leads Insureware’s outstanding statistical department. Comments in this article are the authors own and do not necessarily represent the position of Insureware Pty Ltd.

He completed an Masters degree in Statistics (with First Class Honours) from Massey University, New Zealand.

David has experience in statistical and actuarial analysis along with C++ programming knowledge. Previous projects include working with a Canadian Insurance company to software training and implementation purposes resulting in significant modelling improvements (regions can be modelled within a working day allowing analysts to focus on providing extracted insights to management).

David studied the art of Shaolin Kempo for over nine years, holds a second degree black belt, and is qualified in the use of Okinawan weaponry. He is also interested in music (piano), literature, photography, and self sufficiency. He also has two children on the autism spectrum.

Analysis of “Deal or No Deal” results

Deal or No Deal

My son, Jonathan, loves game-shows, and his current favourite is Deal or No Deal, the Australian version. It has been airing now for over ten years, and there is at least one episode available every weeknight on New Zealand television. I often watch it with him as it is a nice time to spend together. We discuss whether people should take the deal or not, and guess what the bank offer will be. There are other followers of the programme, equally devoted, and I am grateful to Paul Corfiatis and his mum who fastidiously collected data for all the 215 programmes in 2009 on the final takings, the case chosen and the case containing the $200,000. In this post I analyse this data, and give some ideas of how this can be used in teaching.

Deal or No Deal, explained

You can find out ALL about Deal or No Deal on Wikipedia. I was excited to see our New Zealand radio gameshow, “The Money or the Bag” given as an antecedent.  There are numerous incarnations of the game. The basic idea is that there are 26 cases, containing a range of money values from 50c to $200,000. The money values are randomly assigned and their allocation unknown to the contestant and the “banker”. The contestant chooses one of the cases, and chats to the host, Andrew O’Keefe, about what they will do with the money when they win. The usual responses are to have a big wedding or travel. As the programme is filmed in Melbourne, often second generation Australians are wanting to visit their parents’ homeland.   Usually the contestant has a friend or family member as a podium player, who interacts as part of the banter. In the first round, the player chooses six cases to open, thus gaining information about the possible value in their case. At the end of the round, the banker offers a sum of money to buy back the case from the contestant, who must choose, “Deal” (take the money) or “No Deal”, keep the case and its contents. In the second round five cases are opened and then there is another bank offer. This continues until the sixth round, and from then the cases are opened one at a time, with an offer made after each one. The player either takes the deal at some point, or holds out until the end, at which point they take the contents of the case. There are other variants on this basic game, to add variety.

Human aspects

My son is blind and has autism, and finds much to like about this programme. He likes the order of it all – every night, a very similar drama is played out, and he can understand exactly what is happening. He also likes the agony and the joy. He gets very excited when the case containing $200,000 is opened with the special sound effect, and Andrew says, “Oh No”. He likes hearing about the people, and their lives and he likes that you never know how much you might win.

I also like the drama and the joy, but I’d rather not watch when it is going badly. I like it because it is an insight into people’s perceptions of chance. Like many people, I yell at the screen, telling them to take the deal when we see them being reckless, but I am usually happy when their foolish decisions turn out well.  To me it is a true reality show – not because the situation is in any way like reality, but because the people are authentic in their responses. I have been known to weep when a nice person wins a sizeable amount of money. One day I would love to go on the show, as I know how much joy that would bring Jonathan, to be a part of it.

Part of the appeal is the collective experience of it all. The podium players, the audience and the people at home feel connected to the main contestant. One episode that Jonathan loves to tell people about is with Josh Sharpe who was REALLY unlucky. You can see that here on YouTube:

The probability

The probability calculation for Deal or No Deal is very simple. The contestant has one chance in twenty-six that their case contains the big prize. They have four chances in twenty-six that their case contains a prize of $50,000 or more. The expected value of their prize, if they hold onto their case to the end, is about $19,900 (valuing the car at $30,000). When the dealer makes an offer, it is often around the expected value of the remaining unopened cases. (The average amount left.) There are times when the offer is considerably lower or higher than the expected value, which seems to be in an effort to push the contestant one way or the other. Contestants very seldom take the deal in the early rounds of the game.

There are a number of interesting questions we can explore:

  • What is the distribution of the actual outcomes for contestants?
  • How often do contestants do better than what is in their case?
  • Are there any “lucky” cases that contain the big prize more often than others?

To explore these questions I am using the data so diligently collected by Paul Corfiatis. I will use data from games with the regular list of prizes, not “Fantastic Four”, which has some more high value cases.

What is the actual outcome for the contestants?

The following graph shows the amount of money the contestants win, either by taking the deal or hanging out for the case.

Deal_or_No_Deal_outcomes

You could have an interesting discussion about the factors to account for in looking at this. You would expect the mean to be lower for the “case” prizes, as they tend to be people who have kept going to the bitter end. There is a very large standard deviation.

Here is a table of results:

Case Deal Either case or deal
Number of instances 53 146 199
Mean $6139 $21,044 $17,075
Median $500 $18,350 $15,000
Standard Deviation $13,740 $14,499 $15,721
Minimum $0.50 $950 $0.50
Maximum $50,000 $100,000 $100,000

How often do contestants do better than what is in their case?

For this I calculated the prize less the amount that was in their case. The mean value was $1082, with a median of $9969.50, minimum of -$170,050 and a maximum of $99,995. Contestants who took the deal, did better, 106 times out of 146, or 73% of the time.

Lucky Cases

And of course the one to make the statisticians smile – are there any lucky cases?

Here is a graph of the distribution of cases that held the $200,000. I am tempted to make glib comments about how clearly 14 is a lucky case, so you should pick that one, but then, maybe you should pick 19, as it hasn’t had the $200,000 much. But as you never know who is going to quote you, I’d better not.

Which case contained the $200,000 in 2007.

Which case contained the $200,000 in 2007.

Educational use for this

Depending on how much you wish to torment your students, and the educational objectives, you could give them the raw data, as provided on the site, and see what they come up with.  Or you could simply present the results given in this post, watch an episode, and discuss what meanings people could take from the data, and what misconceptions might occur.

About blogging

This is the 100th post on “Learn and Teach Statistics and Operations Research”. To celebrate, I am writing about the joys of blogging.

Anyone with an internet connection can blog these days, and do! It is the procrastinator’s “dark playground” to read blogs on pretty much anything you want to know. (For an explanation, with pictures, of the dark playground, where the instant gratification monkey holds sway until the panic monster arrives, see this entertaining post: Why Procrastinators Procrastinate.)

I started to blog to build a reputation for knowing about teaching statistics and operations research. This would lead people to buy our apps, subscribe to our on-line materials and watch my YouTube videos. Many blogs are set up, like this, in order to build credibility and presence on the internet. I’ve found it quite exciting to watch the readership grow, and I particularly love it when people comment. I also like to feel that I am doing some good in the world. The process of writing is also a learning process for me.

Here are some lightly structured thoughts about what I’ve learned over the last 99 posts.

A blog is not a scholarly research paper

As I come from an academic background, I have had to remind myself that a blog is different from a scholarly research paper. A blog isn’t scholarly, it isn’t based on research (unless you can call time in the shower that) and it isn’t on paper.

Blogging rewards bad behaviour.

The more opinionated you are, and the less evidence you use to support your argument, the more readers you get.  You must remove equivocation. Often after I write my first draft, I go through and remove statements like “in my opinion” or  “it seems”.  This is the antithesis of a scholarly paper, which must be carefully stated in balanced and measured tones.

Blogs are personal

It is good to be personal in a blog. In journal articles we avoid the use of first person language as if the paper were somehow written by itself. This can give rise to convoluted sentence structures and endless passive voice. When I write my blog, I talk about my own ideas, and even aspects of my life. I mention side tracks, and give a little bit of myself. And I prefer to read blogs that have a bit of the author in them. I think you need a little touch of narcissism to enjoy blogging.

Quantity is more important than quality

Volume in blogging dominates quality. Some might argue that this is also true for academic papers. In a blog you are better to dash off one opinion piece a week, than put the same effort into one scholarly paper. If one falls flat, it really doesn’t matter.

Blogs give instant gratification

Blogs have a quick turn-around, ideal for people with short attention spans who want instant gratification. In academia the delay between doing the research and seeing it in print is measured in years. By the time an article has been through the review process, you have almost forgotten why you did the research in the first place. And don’t really care anymore. But when you blog and click “Publish”, it is out there in the world for all to see.

People read blogs

People read your blog. It is an amazing feeling to send out my thoughts into the world and watch the viewing stats on WordPress, knowing that hundreds and sometimes even thousands of people are reading my opinion, literally all over the world. And sometimes I even get emails from fans, telling how my post has helped them or inspired them to work or do research in the area of statistics education. Or else I find that an educational institution has set a link to one of my posts for their students to read. In contrast I wonder if anyone has ever read my journal articles, apart from the reviewers. Not only do people read your blog, but you can see where they live and what they read, and even what search engine terms brought you to the blog. Some search terms boggle the mind, first that someone entered them, and secondly that they led to my blog! The term “rocks” has led to my site 66 times in the last two years, which I am sure was disappointing for the searcher. The most common search term is “causation”.

Blogging does not get you promoted

Though blogging is fun and great for attention-seekers, it does not improve your PBRF ratings (in NZ) or whatever the measure of publication activity is in a specific country. Nor does blogging count for promotion or tenure. This may be simply a matter of time to allow attitudes to change, as erudite blogs can get scientific findings out into the public domain far more rapidly than the old print-based system.

People can be mean

A blogger needs to have a thick skin. I don’t yet, and have to remind myself that I didn’t research my article, so it is only fair for people to offer opposing views. In fact, one the great qualities of a blog is that anyone can respond and improve the quality of the blog. I love it when people leave comments; it is the emailed “hate-messages” that are a bit upsetting.

Keynote speaker

One spin off of a successful blog is that you get asked to be a keynote speaker.

Actually I’m kidding on that one. I’d love to be a keynote speaker, and I’m pretty sure I could entertain a crowd and give them something to think about for an hour or so, but it hasn’t happened. Yet. Any invitations?

Proving causation

Aeroplanes cause hot weather

In Christchurch we have a weather phenomenon known as the “Nor-wester”, which is a warm dry wind, preceding a cold southerly change. When the wind is from this direction, aeroplanes make their approach to the airport over the city. Our university is close to the airport in the direct flightpath, so we are very aware of the planes. A new colleague from South Africa drew the amusing conclusion that the unusual heat of the day was caused by all the planes flying overhead.

Statistics experts and educators spend a lot of time refuting claims of causation. “Correlation does not imply causation” has become a catch cry of people trying to avoid the common trap. This is a great advance in understanding that even journalists (notoriously math-phobic) seem to have caught onto. My own video on important statistical concepts ends with the causation issue. (You can jump to it at 3:51)

So we are aware that it is not easy to prove causation.

In order to prove causation we need a randomised experiment. We need to make random any possible factor that could be associated, and thus cause or contribute to the effect.

There is also the related problem of generalizability. If we do have a randomised experiment, we can prove causation. But unless the sample is also a random representative sample of the population in question, we cannot infer that the results will also transfer to the population in question. This is nicely illustrated in this matrix from The Statistical Sleuth by Fred L. Ramsey and Daniel W Schafer.

The relationship between the type of sample and study and the conclusions that may be drawn.

The relationship between the type of sample and study and the conclusions that may be drawn.

The top left-hand quadrant is the one in which we can draw causal inferences for the population.

Causal claims from observational studies

A student posed this question:  Is it possible to prove a causal link based on an observational study alone?

It would be very useful if we could. It is not always possible to use a randomised trial, particularly when people are involved. Before we became more aware of human rights, experiments were performed on unsuspecting human lab rats. A classic example is the Vipeholm experiments where patients at a mental hospital were the unknowing subjects. They were given large quantities of sweets in order to determine whether sugar caused cavities in teeth. This happened into the early 1950s. These days it would not be acceptable to randomly assign people to groups who are made to smoke or drink alcohol or consume large quantities of fat-laden pastries. We have to let people make those lifestyle choices for themselves. And observe. Hence observational studies!

There is a call for “evidence-based practice” in education to follow the philosophy in medicine. But getting educational experiments through ethics committee approval is very challenging, and it is difficult to use rats or fruit-flies to impersonate the higher learning processes of humans. The changing landscape of the human environment makes it even more difficult to perform educational experiments.

To find out the criteria for justifying causal claims in an observational study I turned to one of my favourite statistics text-books, Chance Encounters by Wild and Seber  (page 27). They cite the Surgeon General of the United States. The criteria for the establishment of a cause and effect relationship in an epidemiological study are the following:

  1. Strong relationship: For example illness is four times as likely among people exposed to a possible cause as it is for those who are not exposed.
  2. Strong research design
  3. Temporal relationship: The cause must precede the effect.
  4. Dose-response relationship: Higher exposure leads to a higher proportion of people affected.
  5. Reversible association: Removal of the cause reduces the incidence of the effect.
  6. Consistency: Multiple studies in different locations producing similar effects
  7. Biological plausibility: there is a supportable biological mechanism
  8. Coherence with known facts.

Teaching about causation

In high school, and entry-level statistics courses, the focus is often on statistical literacy. This concept of causation is pivotal to correct understanding of what statistics can and cannot claim. It is worth spending some time in the classroom discussing what would constitute reasonable proof and what would not. In particular it is worthwhile to come up with alternative explanations for common fallacies, or even truths in causation. Some examples for discussion might be drink-driving and accidents, smoking and cancer, gender and success in all number of areas, home game advantage in sport, the use of lucky charms, socks and undies. This also ties nicely with probability theory, helping to tie the year’s curriculum together.

Absolute and Relative Risk

It is important that citizens can make sense out of the often outrageous claims of advertisers and pro-screening advocates.  It isn’t what they say, but how they say it. What looks like a very large and scary increase in risk, can in fact make very little practical difference. Conversely a large risk can be made to look smaller through the manner in which it is communicated.

I found a wonderful set of notes on the Census at School site, presented as a powerpoint file.

I also found several very interesting and educational sites about risk.

This first one explains about risk and relative risk: Science blog on Cancer Research UK

This one also includes Number needed to treat. Patient Health UK.

And a here is a great summary and set of exercises at the Auckland Maths Association website. You need to scroll down to “Relative Risk Resources”. (I found this after writing the rest of the blog, and it pretty much says what I say, but more succinctly!)

Teaching about Risk

Risk is a great topic for teaching about probability, percentages and perception.

It’s what’s on the bottom that counts!

In exploring risk, there are several distinct processes needed. Depending on the format in which the information is given, students may need to construct their own frequency table, or interpret the one provided. From the frequency table they must calculate the probability, making sure that they choose the correct denominator. Then if they are looking for relative risk, they need to make sure that they again choose the correct denominator. For some reason, the numerator is usually easier. But what can be tricky is the denominator.

We can use as an example the increase in probability of passing a particular statistics course if students use our Statistics Learning Centre materials to help them. We haven’t collected any data yet, so these figures are aspirational (as in a work of fiction!). Because we are talking about risk, we have to frame the outcome in negative terms. We would not talk about the risk of passing a course, but rather of failing one. So we will say that students who use StatsLC materials reduce their risk of failing by 66.7% percent. That is pretty impressive, but how much better it sounds if we frame it in terms of how much their risk will increase if they decide not to use the wonderful materials from StatsLC. Their risk of failure increases by 200%. That sounds pretty drastic.

But what we have failed to mention is the absolute risk, which is the proportion of students who fail their stats courses with and without the help of StatsLC. Here are some pairs of absolute risks that will give the results given:

All of the following sets of numbers show a 200% increase in risk of failure for students who do not use StatsLC materials.

Scenario

Risk of failing, when using StatsLC materials

Risk of failing when they don’t use StatsLC materials

Actual increase in risk of failing.

A

1%

3%

2%

B

10%

30%

20%

C

20%

60%

40%

In Scenario A, the pass-rate for the statistics course has gone from 97% to 99%. In scenario B, the pass-rate has gone from 70% to 90%, and in Scenario C, the pass-rate has gone from 40% to 80%. All of these scenarios could accurately be described by the same change in relative risk. They all double the risk of failing if the student does not use StatsLC.

This is really at the end of the story, based on what is reported. But if we wish to find out what is really going on, the best idea is to build a table of natural frequencies. These are great for calculating conditional probabilities by stealth.

Here is a table of natural frequencies for Scenario C above, using 1000 as our total number of people. Before we fill it out, we also need to know how many people used Statistics Learning Centre materials. 30% of students did NOT use StatsLC materials.

Pass

Fail

Total in category

Use StatsLC

80% of 700 = 560

20% of 700 = 140

700

Do not use StatsLC

40% of 300 = 120

60% of 300 = 180

300

Total pass or fail

680

320

1000

From this table, all manner of statistics can be computed.

What proportion of students who passed, used the StatsLC materials?

The answer is (the number of people who passed AND used StatsLC materials)/( the number of people who passed) = 560/680 =82%. It is important to find the correct denominator.

Then when people calculate relative risk, it is important to be careful about choosing the baseline.

Another question might be, by how much does your risk of failure decrease, in relative terms, if you use the StatsLC materials?

The first step is to find the decrease in absolute terms. The risk of failure, not using StatsLC = 0.6. The risk of failure when using StatsLC has decreased to 0.2. That is an absolute decrease in risk of 0.4. Then we need to express this relative to the baseline. As we talked about the decrease in risk, it will be compared with the larger number, or 0.6, the risk of failing when using the StatsLC materials. So 0.4/0.6 = 0.667 or 66.7%. However, if we were talking about the increase in risk for NOT using StatsLC materials, then we would find 0.4/0.2 = 200%.

A great way to develop interaction and group discussion would be to give individuals in the group different information that is needed for the computation. Later on you could include one wrong “fact”, which they would need to ferret out. Another possibility would be to give students information about different scenarios that they need to present in the best or worst possible light.

These are great teaching opportunities, and worthwhile for everyday life.  It is a good thing they have been included in the NZ curriculum for year 12.

A note to regular readers – I will probably be posting less frequently for a while, but feel free to read back over some of my previous 95 posts if you miss the weekly rant. ;)

Those who can, teach statistics

The phrase I despise more than any in popular use (and believe me there are many contenders) is “Those who can, do, and those who can’t, teach.” I like many of the sayings of George Bernard Shaw, but this one is dismissive, and ignorant and born of jealousy. To me, the ability to teach something is a step higher than being able to do it. The PhD, the highest qualification in academia, is a doctorate. The word “doctor” comes from the Latin word for teacher.

Teaching is a noble profession, on which all other noble professions rest. Teachers are generally motivated by altruism, and often go well beyond the requirements of their job-description to help students. Teachers are derided for their lack of importance, and the easiness of their job. Yet at the same time teachers are expected to undo the ills of society. Everyone “knows” what teachers should do better. Teachers are judged on their output, as if they were the only factor in the mix. Yet how many people really believe their success or failure is due only to the efforts of their teacher?

For some people, teaching comes naturally. But even then, there is the need for pedagogical content knowledge. Teaching is not a generic skill that transfers seamlessly between disciplines. You must be a thinker to be a good teacher. It is not enough to perpetuate the methods you were taught with. Reflection is a necessary part of developing as a teacher. I wrote in an earlier post, “You’re teaching it wrong”, about the process of reflection. Teachers need to know their material, and keep up-to-date with ways of teaching it. They need to be aware of ways that students will have difficulties. Teachers, by sharing ideas and research, can be part of a communal endeavour to increase both content knowledge and pedagogical content knowledge.

There is a difference between being an explainer and being a teacher. Sal Khan, maker of the Khan Academy videos, is a very good explainer. Consequently many students who view the videos are happy that elements of maths and physics that they couldn’t do, have been explained in such a way that they can solve homework problems. This is great. Explaining is an important element in teaching. My own videos aim to explain in such a way that students make sense of difficult concepts, though some videos also illustrate procedure.

Teaching is much more than explaining. Teaching includes awakening a desire to learn and providing the experiences that will help a student to learn.  In these days of ever-expanding knowledge, a content-driven approach to learning and teaching will not serve our citizens well in the long run. Students need to be empowered to seek learning, to criticize, to integrate their knowledge with their life experiences. Learning should be a transformative experience. For this to take place, the teachers need to employ a variety of learner-focussed approaches, as well as explaining.

It cracks me up, the way sugary cereals are advertised as “part of a healthy breakfast”. It isn’t exactly lying, but the healthy breakfast would do pretty well without the sugar-filled cereal. Explanations really are part of a good learning experience, but need to be complemented by discussion, participation, practice and critique.  Explanations are like porridge – healthy, but not a complete breakfast on their own.

Why statistics is so hard to teach

“I’m taking statistics in college next year, and I can’t wait!” said nobody ever!

Not many people actually want to study statistics. Fortunately many people have no choice but to study statistics, as they need it. How much nicer it would be to think that people were studying your subject because they wanted to, rather than because it is necessary for psychology/medicine/biology etc.

In New Zealand, with the changed school curriculum that gives greater focus to statistics, there is a possibility that one day students will be excited to study stats. I am impressed at the way so many teachers have embraced the changed curriculum, despite limited resources, and late changes to assessment specifications. In a few years as teachers become more familiar with and start to specialise in statistics, the change will really take hold, and the rest of the world will watch in awe.

In the meantime, though, let us look at why statistics is difficult to teach.

  1. Students generally take statistics out of necessity.
  2. Statistics is a mixture of quantitative and communication skills.
  3. It is not clear which are right and wrong answers.
  4. Statistical terminology is both vague and specific.
  5. It is difficult to get good resources, using real data in meaningful contexts.
  6. One of the basic procedures, hypothesis testing, is counter-intuitive.
  7. Because the teaching of statistics is comparatively recent, there is little developed pedagogical content knowledge. (Though this is growing)
  8. Technology is forever advancing, requiring regular updating of materials and teaching approaches.

On the other hand, statistics is also a fantastic subject to teach.

  1. Statistics is immediately applicable to life.
  2. It links in with interesting and diverse contexts, including subjects students themselves take.
  3. Studying statistics enables class discussion and debate.
  4. Statistics is necessary and does good.
  5. The study of data and chance can change the way people see the world.
  6. Technlogical advances have put the power for real statistical analysis into the hands of students.
  7. Because the teaching of statistics is new, individuals can make a difference in the way statistics is viewed and taught.

I love to teach. These days many of my students are scattered over the world, watching my videos (for free) on YouTube. It warms my heart when they thank me for making something clear, that had been confusing. I realise that my efforts are small compared to what their teacher is doing, but it is great to be a part of it.

On-line learning and teaching resources

Twenty-first century Junior Woodchuck Guidebook

I grew up reading Donald Duck comics. I love the Junior Woodchucks, and their Junior Woodchuck Guidebook. The Guidebook is a small paperback book, containing information on every conceivable subject, including geography, mythology, history, literature and the Rubaiyat of Omar Khayyam.  In our family, when we want to know something or check some piece of information, we talk about consulting the Junior Woodchuck Guidebook. (Imagine my joy when I discovered that a woodchuck is another name for a groundhog, the star of my favourite movie!) What we are referring to is the internet, the source of all possible information! Thanks to search engines, there is very little we cannot find out on the internet. And very big thanks to Wikipedia, to which I make an annual financial contribution, as should all who use it and can afford to.

You can learn just about anything on the internet. Problem is, how do you know what is good? And how do you help students find good stuff? And how do you use the internet wisely? And how can it help us as learners and teachers of statistics and operations research? These questions will take more than my usual 1000 words, so I will break it up a bit. This post is about the ways the internet can help in teaching and learning. In a later post I will talk about evaluating resources, and in particular multimedia resources.

Context

Both the disciplines in which I am interested, statistics and operations research, apply mathematical and analytic methods to real-world problems. In statistics we are generally trying to find things out, and in operations research we are trying to make them better. Either way, the context is important. The internet enables students to find background knowledge regarding the context of the data or problem they are dealing with. It also enables instructors to write assessments and exercises that have a degree of veracity to them even if the actual raw data proves elusive. How I wish people would publish standard deviations as well as means when reporting results!

Data

Which brings us to the second use for on-line resources. Real problems with real data are much more meaningful for students, and totally possible now that we don’t need to calculate anything by hand. Sadly, it is more difficult than first appears to find good quality raw data to analyse, but there is some available. You can see some sources in a previous post and the helpful comments.

Explanations

If you are struggling to understand a concept, or to know how to teach or explain it, do a web search. I have found some great explanations, and diagrams especially, that have helped me. Or I have discovered a dearth of good diagrams, which has prompted me to make my own.

Video

Videos can help with background knowledge, with explanations, and with inspiring students with the worth of the discipline. The problem with videos is that it takes a long time to find good ones and weed out the others. One suggestion is to enlist the help of your students. They can each watch two or three videos and decide which are the most helpful. The teacher then watches the most popular ones to check for pedagogical value. It is great when you find a site that you can trust, but even then you can’t guarantee the approach will be compatible with your own.

Social support

I particularly love Twitter, from which I get connection with other teachers and learners, and ideas and links to blogs. I belong to a Facebook group for teachers of statistics in New Zealand, and another Facebook group called “I love Operations Research”. These wax and wane in activity, and can be very helpful at times. Students and teachers can gain a lot from social networking.

Software

There is good open-source software available, and 30-day trial versions for other software. Many schools in New Zealand use the R-based iNZight collection of programmes, which provide purpose-built means for timeseries analysis, bootstrapping and line fitting.

Answers to questions

The other day I lost the volume control off my toolbar. (Windows Vista, I’m embarrassed to admit). So I put in the search box “Lost my volume control” and was directed to a YouTube video that took me step-by-step through the convoluted process of reinstating my volume control! I was so grateful I made a donation. Just about any computer related question can be answered through a search.

Interactive demonstrations

I love these. There are two sites I have found great:

The National Library of Virtual Manipulatives, based in Utah.

NRich – It has some great ideas in the senior statistics area. From the UK.

A problem with some of these is the use of Flash, which does not play on all devices. And again – how do we decide if they are any good or not?

On-line textbooks

Why would you buy a textbook when you can get one on-line. I routinely directed my second-year statistical methods for business students to “Concepts and Applications of Inferential Statistics”. I’ve found it just the right level. Another source is Stattrek. I particularly like their short explanations of the different probability distributions.

Practice quizzes

There aren’t too many practice quizzes  around for free. Obviously, as a provider of statistical learning materials, I believe quizzes and exercises have merit for practice with immediate and focussed feedback. However, it can be very time-consuming to evaluate practice quizzes, and some just aren’t very good. On the other hand, some may argue that any time students spend learning is better than none.

Live help

There are some places that provide live, or slightly delayed help for students. I got hooked into a very fun site where you earned points by helping students. Sadly I can’t find it now, but as I was looking I found vast numbers of on-line help sites, often associated with public libraries. And there are commercial sites that provide some free help as an intro to their services. In New Zealand there is the StudyIt service, which helps students preparing for assessments in the senior high school years. At StatsLC we provide on-line help as part of our resources, and will be looking to develop this further. From time to time I get questions as a result of my YouTube videos, and enjoy answering them ,unless I am obviously doing someone’s homework! I also discovered “ShowMe” which looks like a great little iPad app, that I can use to help people more.

This has just been a quick guide to how useful the internet can be in teaching and learning. Next week I will address issues of quality and equity.

How to learn statistics (Part 2)

Some more help (preaching?) for students of statistics

Last week I outlined the first five principles to help people to learn and study statistics.

They focussed on how you need to practise in order to be good at statistics and you should not wait until you understand it completely before you start applying. I sometimes call this suspending disbelief. Next I talked about the importance of context in a statistical investigation, which is one of the ways that statistics is different from pure mathematics. And finally I stressed the importance of technology as a tool, not only for doing the analysis, but for exploring ideas and gaining understanding.

Here are the next five principles (plus 2):

6. Terminology is important and at times inconsistent

There are several issues with regard to statistical terminology, and I have written a post with ideas for teachers on how to teach terminology.

One issue with terminology is that some words that are used in the study of statistics have meanings in everyday life that are not the same. A clear example of this is the word, “significant”. In regular usage this can mean important or relevant, yet in statistics, it means that there is evidence that an effect that shows up in the sample also exists in the population.

Another issue is that statistics is a relatively young science and there are inconsistencies in terminology. We just have to live with that. Depending on the discipline in which the statistical analysis is applied or studied, different terms can mean the same thing, or very close to it.

A third language problem is that mixed in with the ambiguity of results, and judgment calls, there are some things that are definitely wrong. Teachers and examiners can be extremely picky. In this case I would suggest memorising the correct or accepted terminology for confidence intervals and hypothesis tests. For example I am very fussy about the explanation for the R-squared value in regression. Too often I hear that it says how much of the dependent variable is explained by the independent variable. There needs to be the word “variation” inserted in there to make it acceptable. I encourage my students to memorise a format for writing up such things. This does not substitute for understanding, but the language required is precise, so having a specific way to write it is fine.

This problem with terminology can be quite frustrating, but I think it helps to have it out in the open. Think of it as learning a new language, which is often the case in new subject. Use glossaries, to make sure you really do know what a term means.

7. Discussion is important

This is linked with the issue of language and vocabulary. One way to really learn something is to talk about it with someone else and even to try and teach it to someone else. Most teachers realise that the reason they know something pretty well, is because they have had to teach it. If your class does not include group work, set up your own study group. Talk about the principles as well as the analysis and context, and try to use the language of statistics. Working on assignments together is usually fine, so long as you write them up individually, or according to the assessment requirements.

8. Written communication skills are important

Mathematics has often been a subject of choice for students who are not fluent in English. They can perform well because there is little writing involved in a traditional mathematics course. Statistics is a different matter, though, as all students should be writing reports. This can be difficult at the start, but as students learn to follow a structure, it can be made more palatable. A statistics report is not a work of creative writing, and it is okay to use the same sentence structure more than once. Neither is a statistics report a narrative of what you did to get to the results. Generous use of headings makes a statistical report easier to read and to write. A long report is not better than a short report, if all the relevant details are there.

9. Statistics has an ethical and moral aspect

This principle is interesting, as many teachers of statistics come from a mathematical background, and so have not had exposure to the ethical aspects of research themselves. That is no excuse for students to park their ethics at the door of the classroom. I will be pushing for more consideration of ethical aspects of research as part of the curriculum in New Zealand. Students should not be doing experiments on human subjects that involve delicate subjects such as abuse, or bullying. They should not involve alcohol or other harmful substances. They should be aware of the potential to do harm, and make sure that any participants have been given full information and given consent. This can be quite a hurdle, but is part of being an ethical human being. It also helps students to be more aware when giving or withholding consent in medical and other studies.

10. The study of statistics can change the way you view the world

Sometimes when we learn something at school, it stays at school and has no impact on our everyday lives. This should not be the case with the study of statistics. As we learn about uncertainty and variation we start to see this in the world around us. When we learn about sampling and non-sampling errors, we become more critical of opinion polls and other research reported in the media. As we discover the power of statistical analysis and experimentation, we start to see the importance of evidence-based practice in medicine, social interventions and the like.

11. Statistics is an inherently interesting and relevant subject.

And it can be so much fun. There is a real excitement in exploring data, and becoming a detective. If you aren’t having fun, you aren’t doing it right!

12. Resources from Statistics Learning Centre will help you learn.

Of course!

How to study statistics (Part 1)

To students of statistics

Most of my posts are directed at teachers and how to teach statistics. The blog this week and next is devoted to students. I present principles that will help you to learn statistics. I’m turning them into a poster, which I will make available for you to printing later. I’d love to hear from other teachers as I add to my list of principles.

1. Statistics is learned by doing

One of the best predictors of success in any subject is how much time you spent on it. If you want to learn statistics, you need to put in time. It is good to read the notes and the textbook, and to look up things on the internet and even to watch Youtube videos if they are good ones. But the most important way to learn statistics is by doing. You need to practise at the skills that are needed by a statistician, which include logical thinking, interpretation, judgment and writing. Your teacher should provide you with worthwhile practice activities, and helpful timely feedback. Good textbooks have good practice exercises. On-line materials have many practice exercises.

Given a choice, do the exercises that have answers available. It is very important that you check what you are doing, as it is detrimental to practise something in the wrong way. Or if you are using an on-line resource, make sure you check your answers as you go, so that you gain from the feedback and avoid developing bad habits.

So really the first principle should really be “statistics is learned by doing correctly.

2. Understanding comes with application, not before.

Do not wait until you understand what you are doing before you get started. The understanding comes as you do the work. When we learn to speak, we do not wait until we understand grammatical structure before saying anything. We use what we have to speak and to listen, and as we do so we gain an understanding of how language works.  I have found that students who spent a lot of time working through the process of calculating conditional probabilities for screening tests grew to understand the “why” as well as the “how” of the process. Repeated application of using Excel to fit a line to bivariate data and explaining what it meant, enabled students to understand and internalise what a line means. As I have taught statistics for two decades, my own understanding has continued to grow.

There is a proviso. You need to think about what you are doing, and you need to do worthwhile exercises. For example, mechanically calculating the standard deviation of a set of numbers devoid of context will not help you understand standard deviation. Looking at graphs and trying to guess what the standard deviation is, would be a better exercise. Then applying the value to the context is better still.

Applying statistical principles to a wide variety of contexts helps us to discern what is specific to a problem and what is general for all problems. This brings us to the next principle.

3. Spend time exploring the context.

In a statistical analysis, context is vital, and often very interesting. You need to understand the problem that gave rise to the investigation and collection of the data. The context is what makes each statistical investigation different. Statisticians often work alongside other researchers in areas such as medicine, psychology, biology and geology, who provide the contextual background to the problem. This provides a wonderful opportunity for the statistician to learn about a whole range of different subjects. The interplay between the data and context mean that every investigation is different.

In a classroom setting you will not have the subject expert available, but you do need to understand the story behind the data. These days, finding out is possible with a click of a Google or Wikipedia button. Knowing the background to the data helps you to make more sensible judgments – and it makes it more interesting.

4. Statistics is different from mathematics

In mathematics, particularly pure mathematics, context is stripped away in order to reveal the inner pure truth of numbers and logic.  There are applied areas involving mathematics, which are more like statistics, such as operations research and engineering. At school level, one of the things that characterises the study of maths is right and wrong answers, with a minimum of ambiguity. That is what I loved about mathematics – being able to apply an algorithm and get a correct answer. In statistics, however, things are seldom black-and-white.  In statistics you will need to interpret data from the perspective of the real world, and often the answer is not clear. Some people find the lack of certainty in statistics disturbing. There is considerable room for discussion in statistics. Some aspects of statistics are fuzzy, such as what to do with messy data, or which is the “best” model to fit a time series. There is a greater need for the ability to write in statistics, which makes if more challenging for students for whom English is not their native language.

5. Technology is essential

With computers and calculators, all sorts of activities are available to help learn statistics. Graphs and graphics enable exploration that was not possible when graphs had to be drawn by hand. You can have a multivariate data set and explore all the possible relationships with a few clicks. You should always look at the data in a graphical form before setting out to analyse.

Sometimes I would set optional exercises for students to explore the relationship between data, graphs and summary measures. Very few students did so, but when I led them through the same examples one at a time I could see the lights go on. When you are given opportunities to use computing power to explore and learn – do it!

But wait…there’s more

Here we have the first five principles for students learning statistics. Watch this space next week for some more. And do add some in the comments and I will try to incorporate your ideas as well.

Open Letter to Khan Academy about Basic Probability

Khan academy probability videos and exercises aren’t good either

Dear Mr Khan

You have created an amazing resource that thousands of people all over the world get a lot of help from. Well done. Some of your materials are not very good, though, so I am writing this open letter in the hope that it might make some difference. Like many others, I believe that something as popular as Khan Academy will benefit from constructive criticism.

I fear that the reason that so many people like your mathematics videos so much is not because the videos are good, but because their experience in the classroom is so bad, and the curriculum is poorly thought out and encourages mechanistic thinking. This opinion is borne out by comments I have read from parents and other bloggers. The parents love you because you help their children pass tests.  (And these tests are clearly testing the type of material you are helping them to pass!) The bloggers are not so happy, because you perpetuate a type of mathematical instruction that should have disappeared by now. I can’t even imagine what the history teachers say about your content-driven delivery, but I will stick to what I know. (You can read one critique here)

Just over a year ago I wrote a balanced review of some of the Khan Academy videos about statistics. I know that statistics is difficult to explain – in fact one of the hardest subjects to teach. You can read my review here. I’ve also reviewed a selection of videos about confidence intervals, one of which was from Khan Academy. You can read the review here.

Consequently I am aware that blogging about the Khan Academy in anything other than glowing terms is an invitation for vitriol from your followers.

However, I thought it was about time I looked at the exercises that are available on KA, wondering if I should recommend them to high school teachers for their students to use for review. I decided to focus on one section, introduction to probability. I put myself in the place of a person who was struggling to understand probability at school.

Here is the verdict.

First of all the site is very nice. It shows that it has a good sized budget to use on graphics and site mechanics. It is friendly to get into. I was a bit confused that the first section in the Probability and Statistics Section is called “Independent and dependent events”. It was the first section though. The first section of this first section is called Basic Probability, so I felt I was in the right place. But then under the heading, Basic probability, it says, “Can I pick a red frog out of a bag that only contains marbles?” Now I have no trouble with humour per se, and some people find my videos pretty funny. But I am very careful to avoid confusing people with the humour. For an anxious student who is looking for help, that is a bit confusing.

I was excited to see that this section had five videos, and two sets of exercises. I was pleased about that, as I’ve wanted to try out some exercises for some time, particularly after reading the review from Fawn Nguyen on her experience with exercises on Khan Academy. (I suggest you read this – it’s pretty funny.)

So I watched the first video about probability and it was like any other KA video I’ve viewed, with primitive graphics and a stumbling repetitive narration. It was correct enough, but did not take into account any of the more recent work on understanding probability. It used coins and dice. Big yawn. It wastes a lot of time. It was ok. I do like that you have the interactive transcript so you can find your way around.

It dawned on me that nowhere do you actually talk about what probability is. You seem to assume that the students already know that. In the very start of the first video it says,

“What I want to do in this video is give you at least a basic overview of probability. Probability, a word that you’ve probably heard a lot of and you are probably just a little bit familiar with it. Hopefully this will get you a little deeper understanding.”

Later in the video there is a section on the idea of large numbers of repetitions, which is one way of understanding probability. But it really is a bit skimpy on why anyone would want to find or estimate a probability, and what the values actually mean. But it was ok.

The first video was about single instances – one toss of a coin or one roll of a die. Then the second video showed you how to answer the questions in the exercises, which involved two dice. This seemed ok, if rather a sudden jump from the first video. Sadly both of these examples perpetuate the common misconception that if there are, say, 6 alternative outcomes, they will necessarily be equally likely.

Exercises

Then we get to some exercises called “Probability Space” , which is not an enormously helpful heading. But my main quest was to have a go at the exercises, so that is what I did. And that was not a good thing. The exercises were not stepped, but started right away with an example involving two dice and the phrase “at least one of”. There was meant to be a graphic to help me, but instead I had the message “scratchpad not available”. I will summarise my concerns about the exercises at the end of my letter. I clicked on a link to a video that wasn’t listed on the left, called Probability Space and got a different kind of video.

This video was better in that it had moving pictures and a script. But I have problems with gambling in videos like this. There are some cultures in which gambling is not acceptable. The other problem I have is with the term  “exact probability”, which was used several times. What do we mean by “exact probability”? How does he know it is exact? I think this sends the wrong message.

Then on to the next videos which were worked examples, entitled “Example: marbles from a bag, Example: Picking a non-blue marble, Example: Picking a yellow marble.” Now I understand that you don’t want to scare students with terminology too early, but I would have thought it helpful to call the second one, “complementary events, picking a non-blue marble”. That way if a student were having problems with complementary events in exercises from school, they could find their way here. But then I’m not sure who your audience is. Are you sure who your audience is?

The first marble video was ok, though the terminology was sloppy.

The second marble video, called “Example: picking a non-blue marble”, is glacially slow. There is a point, I guess in showing students how to draw a bag and marbles, but… Then the next example is of picking numbers at random. Why would we ever want to do this? Then we come to an example of circular targets. This involves some problem-solving regarding areas of circles, and cancelling out fractions including pi. What is this about? We are trying to teach about probablity so why have you brought in some complication involving the area of a circle?

The third marble video attempts to introduce the idea of events, but doesn’t really. By trying not to confuse with technical terms, the explanation is more confusing.

Now onto some more exercises. The Khan model is that you have to get 5 correct in a row in order to complete an exercise. I hope there is some sensible explanation for this, because it sure would drive me crazy to have to do that. (As I heard expressed on Twitter)

What are circular targets doing in with basic probability?

The first example is a circular target one.  I SO could not be bothered working out the area stuff so I used the hints to find the answer so I could move onto a more interesting example. The next example was finding the probability of a rolling a 4 from a fair six sided die. This is trivial, but would have been not a bad example to start with. Next question involve three colours of marbles, and finding the probability of not green. Then another dart-board one. Sigh. Then another dart board one. I’m never going to find out what happens if I get five right in a row if I don’t start doing these properly. Oh now – it gave me circumference. SO can’t be bothered.

And that was the end of Basic probability. I never did find out what happens if I get five correct in a row.

Venn diagrams

The next topic is called “Venn diagrams and adding probabilities “. I couldn’t resist seeing what you would do with a Venn diagram. This one nearly reduced me to tears.

As you know by now, I have an issue with gambling, so it will come as no surprise that I object to the use of playing cards in this example. It makes the assumption that students know about playing cards. You do take one and a half minutes to explain the contents of a standard pack of cards.  Maybe this is part of the curriculum, and if so, fair enough. The examples are standard – the probability of getting a Jack of Hearts etc. But then at 5:30 you start using Venn diagrams. I like Venn diagrams, but they are NOT good for what you are teaching at this level, and you actually did it wrong. I’ve put a comment in the feedback section, but don’t have great hopes that anything will change. Someone else pointed this out in the feedback two years ago, so no – it isn’t going to change.

Khan Venn diagram

This diagram is misleading, as is shown by the confusion expressed in the questions from viewers. There should be a green 3, a red 12, and a yellow 1.

Now Venn diagrams seem like a good approach in this instance, but decades of experience in teaching and communicating complex probabilities has shown that in most instances a two-way table is more helpful. The table for the Jack of Hearts problem would look like this:

Jacks Not Jacks Total
Hearts 1 12 13
Not Hearts 3 36 39
Total 4 48 52

(Any teachers reading this letter – try it! Tables are SO much easier for problem solving than Venn diagrams)

But let’s get down to principles.

The principles of instruction that KA have not followed in the examples:

  • Start easy and work up
  • Be interesting in your examples – who gives a flying fig about two dice or random numbers?
  • Make sure the hardest part of the question is the thing you are testing. This is particularly violated with the questions involving areas of circles.
  • Don’t make me so bored that I can’t face trying to get five in a row and not succeed.

My point

Yes, I do have one. Mr Khan you clearly can’t be stopped, so can you please get some real teachers with pedagogical content knowledge to go over your materials systematically and make them correct. You have some money now, and you owe it to your benefactors to GET IT RIGHT. Being flippant and amateurish is fine for amateurs but you are now a professional, and you need to be providing material that is professionally produced. I don’t care about the production values – keep the stammers and “lellows” in there if you insist. I’m very happy you don’t have background music as I can’t stand it myself. BUT… PLEASE… get some help and make your videos and exercises correct and pedagogically sound.

Dr Nic

PS – anyone else reading this letter, take a look at the following videos for mathematics.

And of course I think my own Statistics Learning Centre videos are pretty darn good as well.

Other posts about concerns about Khan:

Another Open Letter to Sal ( I particularly like the comment by Michael Paul Goldenberg)

Breaking the cycle (A comprehensive summary of the responses to criticism of Khan