ASA Connect

------------------------------
Mark Chamness
Head of Data Science
Invictus Growth Partners
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When I teach intro to stats classes, I use SPSS or Excel. I see absolutely NO need to use R, Python, SAS, etc. Teaching programming at that point is pretty pointless. It takes away time that SHOULD be spent learning topics or reinforcing ideas that were already taught. If someone wants to teach programming as an additional lab, great. Integrating it into the class, no thank you. 

Having taken many stats classes where the profs insisted that we had to learn how to code, they wasted many hours doing so and usually made mistakes along the way and spent a lot of time trying to figure out what went wrong. In every case I can think of, the "special package" we "need" to use in R and according to the profs, ONLY found in R, is available in every other no code stats software for the last 3-5 versions. 

As a statistician, I have NEVER encountered a data set where I needed some package or routine that ONLY exists in R, Python, SAS, etc. SPSS or JMP work really well for everything I have ever done. I find some of the methods in Design Expert to be quite useful too. In fact, the optimization portion of Design Expert does something that optimization algorithms in R and Python wont do and was the topic of 2 presentations I've given to local ASA groups. 

For giving presentations, those where a stakeholders ask for "on the fly" analyses of the data you are presenting, SPSS or JMP are far superior to R, Python. In fact, a presentation I gave to a VP at a Fortune 500 company,  after going over the analysis I did with JMP, and answered all the questions he had then and there with JMP, he went back to the statisticians and yelled at them FOR using R. 

By forcing or requiring coding languages, especially too early in an academic career, you make statistics seem like elitist endeavor. You'll get students to leave the program or not think a stats minor is worth pursuing. 

When I teach intro to stats classes, I use SPSS or Excel. I see absolutely NO need to use R, Python, SAS, etc. Teaching programming at that point is pretty pointless. It takes away time that SHOULD be spent learning topics or reinforcing ideas that were already taught. If someone wants to teach programming as an additional lab, great. Integrating it into the class, no thank you. 

Having taken many stats classes where the profs insisted that we had to learn how to code, they wasted many hours doing so and usually made mistakes along the way and spent a lot of time trying to figure out what went wrong. In every case I can think of, the "special package" we "need" to use in R and according to the profs, ONLY found in R, is available in every other no code stats software for the last 3-5 versions. 

As a statistician, I have NEVER encountered a data set where I needed some package or routine that ONLY exists in R, Python, SAS, etc. SPSS or JMP work really well for everything I have ever done. I find some of the methods in Design Expert to be quite useful too. In fact, the optimization portion of Design Expert does something that optimization algorithms in R and Python wont do and was the topic of 2 presentations I've given to local ASA groups. 

For giving presentations, those where a stakeholders ask for "on the fly" analyses of the data you are presenting, SPSS or JMP are far superior to R, Python. In fact, a presentation I gave to a VP at a Fortune 500 company,  after going over the analysis I did with JMP, and answered all the questions he had then and there with JMP, he went back to the statisticians and yelled at them FOR using R. 

By forcing or requiring coding languages, especially too early in an academic career, you make statistics seem like elitist endeavor. You'll get students to leave the program or not think a stats minor is worth pursuing. 

Interesting thoughts...

Let's go way back to the advent of the calculator. Why did we need to learn how to add and multiply if a calculator could do it for us? Yet here we are in 2024 and students in grammar school are still learning those tables. You would be aghast if you went to a tax professional and they didn't use a computer or calculator - same with a grocery store clerk. Yet, we still teach, memorize, and have competitions on who can compute the fastest.  Why? (and why Algebra 2? taking cubed roots and n-th roots... unless you are going into physics, astrophysics, and various similar enterprises, have you ever needed the n-th root of anything in practice? or even needed to understand that concept?)

Before we go suggesting turning the educational world on its head (because AI is the latest and greatest thing since sliced bread)...  let's think about what and why we teach what we teach - and the audience...

Some students - absolutely, teach them "stats for consumers." Those students will likely never go on to "do" statistics, although understanding what they mean and what they can and can't do is great. Do they need to actually calculate anything? JMP or whatever program the teacher uses might be great to demonstrate what is going on - e.g. how a histogram can "change shape" depending on the number of bins, or how the mean can change with outliers, etc).  For younger people who may actually go into statistics as a career?? That class may turn them off. (personally, I loved coming from mathematics - understanding the integration and formulae - but, I'm weird).  Younger minds are so much more tech savvy than I was (am) - so many learn to code when they are in grammar school - for those kids, coding may be the thing that attracts them to the field. (AP stats using a TI calculator, can we twinkle think on that?!). I was sad to see in many high schools that "statistics" is the easy course to take as a senior for kids who don't like math (rather than, Pre Calc in some cases). We are losing so many potential statisticians to this mindset.  

Just thoughts for a mid-day Friday... 

When I teach intro to stats classes, I use SPSS or Excel. I see absolutely NO need to use R, Python, SAS, etc. Teaching programming at that point is pretty pointless. It takes away time that SHOULD be spent learning topics or reinforcing ideas that were already taught. If someone wants to teach programming as an additional lab, great. Integrating it into the class, no thank you. 

Having taken many stats classes where the profs insisted that we had to learn how to code, they wasted many hours doing so and usually made mistakes along the way and spent a lot of time trying to figure out what went wrong. In every case I can think of, the "special package" we "need" to use in R and according to the profs, ONLY found in R, is available in every other no code stats software for the last 3-5 versions. 

As a statistician, I have NEVER encountered a data set where I needed some package or routine that ONLY exists in R, Python, SAS, etc. SPSS or JMP work really well for everything I have ever done. I find some of the methods in Design Expert to be quite useful too. In fact, the optimization portion of Design Expert does something that optimization algorithms in R and Python wont do and was the topic of 2 presentations I've given to local ASA groups. 

For giving presentations, those where a stakeholders ask for "on the fly" analyses of the data you are presenting, SPSS or JMP are far superior to R, Python. In fact, a presentation I gave to a VP at a Fortune 500 company,  after going over the analysis I did with JMP, and answered all the questions he had then and there with JMP, he went back to the statisticians and yelled at them FOR using R. 

By forcing or requiring coding languages, especially too early in an academic career, you make statistics seem like elitist endeavor. You'll get students to leave the program or not think a stats minor is worth pursuing. 

30% of the students I've taught at a college level have been Pre-algebra students. Those that didn't memorize those tables. They struggle with math. As a result, only 40% to 45% of those students even pass... (with most professors) For those that pass, the "A" students pass Algebra 1 about 80% of the time. Those that got an "A-", they pass Algebra 1 about 55% of the time. 

When it comes to teaching with a calculator, my students get to borrow a calculator from me, if they need. My pre-algebra students pass at a rate of about 80%. Depending upon what class they take next, they might take the "Everyday Math" class. Where, depending upon the college/university, students are required to use Excel to do math. Given that "Experience with Office Products" is a job requirement a lot of places, this is a job critical skill. Being able to do all of those calculations in one's head looks impressive. But, there are how many ways to solve each type of problem, and if you get into a mathematically rigorous job, doing things in your head is far less impressive and practical. 

I do agree that we should look at what we teach and really think about it. When I teach Algebra 1, Alg 2 and Pre-calc, and we are dealing with say quadratics, There are how many pages in the textbook discussing how to solve these problems? Yet, most of the problems we give our students in those classes can be done in their head. Then, students get into Chemistry and have a problem like: Ka = [X][X]/[C0-X]. They have to solve for 'X' and none of what I taught them is relevant or useful. The equation: X = (-b +/-sqrt(b^2 - 4ac))/( 2a) can solve this problem. And all the other quadratics. But, we waste time on all those other useless (in the real world) methods. 

When you get into Econ, you can have a profit curve that mimics a quadratic equation. In the equation Y = aX^2 + bX + C, 'C' is the "start up cost" The first value of 'X' is the number of units you need to sell to "Break Even". The second value of X is the number of units you need to sell to stop making money. I believe the total profit is the area under the curve. So, the area bound by the triangle (0,0), (0,C) (X1, 0) is loss. From X1 to X2 is profit. So, I have my students calculate the profit by finding the area under a "half circle" between X1 and X2 and subtract off the area of the triangle. We can then ask a question about, "Should the company make this item?" If the profit is negative, then no. If the profit is "small" maybe. If the profit is "large", then definitely. Having a problem like this, the students get a complete idea about why they learned how to solve a quadratic. 

When it comes to a stats class and the use of say SPSS or JMP vs R, Python, etc, for the reasons I sited, I will choose SPSS or JMP. After taking a class on Numerical Analysis, my resolve to use SPSS or JMP was strengthened. Why? SPSS and JMP hire professionals to write solid code using good programming skills and numerical analysis techniques. If the software uses something like Intel's MKL or AMD's math library, the quality of those algorithms is great!

Meanwhile, in my stats classes, the prof's insist that you get the coefficients for a regression model using partial derivatives. Some will try to write code based upon this idea. Truth is, good software uses a pseudo-inverse. Better software uses all the cores in your CPU to do those calculations. 

For fun, I solved this type of problem with a non-linear solver and the use of random number generation. The non-linear solver according to textbooks should use partial derivatives. In practice, no calculus is used.  

So, if we are going to use something like R or Python, "to show what goes on" or whatever reason, does the user know what the software is doing? Does the user know how many ways you can solve the same problem? Doubt it. Can one come up with more ways to solve those same problems? certainly! 

We definitely need to think about what we teach and why. 

Interesting thoughts...

Let's go way back to the advent of the calculator. Why did we need to learn how to add and multiply if a calculator could do it for us? Yet here we are in 2024 and students in grammar school are still learning those tables. You would be aghast if you went to a tax professional and they didn't use a computer or calculator - same with a grocery store clerk. Yet, we still teach, memorize, and have competitions on who can compute the fastest.  Why? (and why Algebra 2? taking cubed roots and n-th roots... unless you are going into physics, astrophysics, and various similar enterprises, have you ever needed the n-th root of anything in practice? or even needed to understand that concept?)

Before we go suggesting turning the educational world on its head (because AI is the latest and greatest thing since sliced bread)...  let's think about what and why we teach what we teach - and the audience...

Some students - absolutely, teach them "stats for consumers." Those students will likely never go on to "do" statistics, although understanding what they mean and what they can and can't do is great. Do they need to actually calculate anything? JMP or whatever program the teacher uses might be great to demonstrate what is going on - e.g. how a histogram can "change shape" depending on the number of bins, or how the mean can change with outliers, etc).  For younger people who may actually go into statistics as a career?? That class may turn them off. (personally, I loved coming from mathematics - understanding the integration and formulae - but, I'm weird).  Younger minds are so much more tech savvy than I was (am) - so many learn to code when they are in grammar school - for those kids, coding may be the thing that attracts them to the field. (AP stats using a TI calculator, can we twinkle think on that?!). I was sad to see in many high schools that "statistics" is the easy course to take as a senior for kids who don't like math (rather than, Pre Calc in some cases). We are losing so many potential statisticians to this mindset.  

Just thoughts for a mid-day Friday... 

When I teach intro to stats classes, I use SPSS or Excel. I see absolutely NO need to use R, Python, SAS, etc. Teaching programming at that point is pretty pointless. It takes away time that SHOULD be spent learning topics or reinforcing ideas that were already taught. If someone wants to teach programming as an additional lab, great. Integrating it into the class, no thank you. 

Having taken many stats classes where the profs insisted that we had to learn how to code, they wasted many hours doing so and usually made mistakes along the way and spent a lot of time trying to figure out what went wrong. In every case I can think of, the "special package" we "need" to use in R and according to the profs, ONLY found in R, is available in every other no code stats software for the last 3-5 versions. 

As a statistician, I have NEVER encountered a data set where I needed some package or routine that ONLY exists in R, Python, SAS, etc. SPSS or JMP work really well for everything I have ever done. I find some of the methods in Design Expert to be quite useful too. In fact, the optimization portion of Design Expert does something that optimization algorithms in R and Python wont do and was the topic of 2 presentations I've given to local ASA groups. 

For giving presentations, those where a stakeholders ask for "on the fly" analyses of the data you are presenting, SPSS or JMP are far superior to R, Python. In fact, a presentation I gave to a VP at a Fortune 500 company,  after going over the analysis I did with JMP, and answered all the questions he had then and there with JMP, he went back to the statisticians and yelled at them FOR using R. 

By forcing or requiring coding languages, especially too early in an academic career, you make statistics seem like elitist endeavor. You'll get students to leave the program or not think a stats minor is worth pursuing. 

If education policy simply consists of "What do they need in their work etc.?", we've reached a nadir in our outlook as academics.

Very few people, for example, really "need" to know that Brazil is in South America. But if someone graduates high school, let alone college, not knowing that, something is truly amiss.

Regarding cube roots, times tables and so on, it's all part of a big picture in which students develop an intuitive feel for numbers, their sizes and their meanings. It's wonderful to show students how changing the bin size changes the shape of a histogram, say, but in order for them to understand WHY setting too narrow or too wide a bin size is problematic, they MUST have this feeling for numbers

AND...the weakest students are the most in need of this. We can of course take the easy way out and just have them stare at histograms without much real understanding, or we can do our best to lift up the weak ones to the levels of excellent insight that they deserve.

Think twice before signing up for a Cultural Revolution in math.

30% of the students I've taught at a college level have been Pre-algebra students. Those that didn't memorize those tables. They struggle with math. As a result, only 40% to 45% of those students even pass... (with most professors) For those that pass, the "A" students pass Algebra 1 about 80% of the time. Those that got an "A-", they pass Algebra 1 about 55% of the time. 

When it comes to teaching with a calculator, my students get to borrow a calculator from me, if they need. My pre-algebra students pass at a rate of about 80%. Depending upon what class they take next, they might take the "Everyday Math" class. Where, depending upon the college/university, students are required to use Excel to do math. Given that "Experience with Office Products" is a job requirement a lot of places, this is a job critical skill. Being able to do all of those calculations in one's head looks impressive. But, there are how many ways to solve each type of problem, and if you get into a mathematically rigorous job, doing things in your head is far less impressive and practical. 

I do agree that we should look at what we teach and really think about it. When I teach Algebra 1, Alg 2 and Pre-calc, and we are dealing with say quadratics, There are how many pages in the textbook discussing how to solve these problems? Yet, most of the problems we give our students in those classes can be done in their head. Then, students get into Chemistry and have a problem like: Ka = [X][X]/[C0-X]. They have to solve for 'X' and none of what I taught them is relevant or useful. The equation: X = (-b +/-sqrt(b^2 - 4ac))/( 2a) can solve this problem. And all the other quadratics. But, we waste time on all those other useless (in the real world) methods. 

When you get into Econ, you can have a profit curve that mimics a quadratic equation. In the equation Y = aX^2 + bX + C, 'C' is the "start up cost" The first value of 'X' is the number of units you need to sell to "Break Even". The second value of X is the number of units you need to sell to stop making money. I believe the total profit is the area under the curve. So, the area bound by the triangle (0,0), (0,C) (X1, 0) is loss. From X1 to X2 is profit. So, I have my students calculate the profit by finding the area under a "half circle" between X1 and X2 and subtract off the area of the triangle. We can then ask a question about, "Should the company make this item?" If the profit is negative, then no. If the profit is "small" maybe. If the profit is "large", then definitely. Having a problem like this, the students get a complete idea about why they learned how to solve a quadratic. 

When it comes to a stats class and the use of say SPSS or JMP vs R, Python, etc, for the reasons I sited, I will choose SPSS or JMP. After taking a class on Numerical Analysis, my resolve to use SPSS or JMP was strengthened. Why? SPSS and JMP hire professionals to write solid code using good programming skills and numerical analysis techniques. If the software uses something like Intel's MKL or AMD's math library, the quality of those algorithms is great!

Meanwhile, in my stats classes, the prof's insist that you get the coefficients for a regression model using partial derivatives. Some will try to write code based upon this idea. Truth is, good software uses a pseudo-inverse. Better software uses all the cores in your CPU to do those calculations. 

For fun, I solved this type of problem with a non-linear solver and the use of random number generation. The non-linear solver according to textbooks should use partial derivatives. In practice, no calculus is used.  

So, if we are going to use something like R or Python, "to show what goes on" or whatever reason, does the user know what the software is doing? Does the user know how many ways you can solve the same problem? Doubt it. Can one come up with more ways to solve those same problems? certainly! 

We definitely need to think about what we teach and why. 

Interesting thoughts...

Let's go way back to the advent of the calculator. Why did we need to learn how to add and multiply if a calculator could do it for us? Yet here we are in 2024 and students in grammar school are still learning those tables. You would be aghast if you went to a tax professional and they didn't use a computer or calculator - same with a grocery store clerk. Yet, we still teach, memorize, and have competitions on who can compute the fastest.  Why? (and why Algebra 2? taking cubed roots and n-th roots... unless you are going into physics, astrophysics, and various similar enterprises, have you ever needed the n-th root of anything in practice? or even needed to understand that concept?)

Before we go suggesting turning the educational world on its head (because AI is the latest and greatest thing since sliced bread)...  let's think about what and why we teach what we teach - and the audience...

Some students - absolutely, teach them "stats for consumers." Those students will likely never go on to "do" statistics, although understanding what they mean and what they can and can't do is great. Do they need to actually calculate anything? JMP or whatever program the teacher uses might be great to demonstrate what is going on - e.g. how a histogram can "change shape" depending on the number of bins, or how the mean can change with outliers, etc).  For younger people who may actually go into statistics as a career?? That class may turn them off. (personally, I loved coming from mathematics - understanding the integration and formulae - but, I'm weird).  Younger minds are so much more tech savvy than I was (am) - so many learn to code when they are in grammar school - for those kids, coding may be the thing that attracts them to the field. (AP stats using a TI calculator, can we twinkle think on that?!). I was sad to see in many high schools that "statistics" is the easy course to take as a senior for kids who don't like math (rather than, Pre Calc in some cases). We are losing so many potential statisticians to this mindset.  

Just thoughts for a mid-day Friday... 

When I teach intro to stats classes, I use SPSS or Excel. I see absolutely NO need to use R, Python, SAS, etc. Teaching programming at that point is pretty pointless. It takes away time that SHOULD be spent learning topics or reinforcing ideas that were already taught. If someone wants to teach programming as an additional lab, great. Integrating it into the class, no thank you. 

Having taken many stats classes where the profs insisted that we had to learn how to code, they wasted many hours doing so and usually made mistakes along the way and spent a lot of time trying to figure out what went wrong. In every case I can think of, the "special package" we "need" to use in R and according to the profs, ONLY found in R, is available in every other no code stats software for the last 3-5 versions. 

As a statistician, I have NEVER encountered a data set where I needed some package or routine that ONLY exists in R, Python, SAS, etc. SPSS or JMP work really well for everything I have ever done. I find some of the methods in Design Expert to be quite useful too. In fact, the optimization portion of Design Expert does something that optimization algorithms in R and Python wont do and was the topic of 2 presentations I've given to local ASA groups. 

For giving presentations, those where a stakeholders ask for "on the fly" analyses of the data you are presenting, SPSS or JMP are far superior to R, Python. In fact, a presentation I gave to a VP at a Fortune 500 company,  after going over the analysis I did with JMP, and answered all the questions he had then and there with JMP, he went back to the statisticians and yelled at them FOR using R. 

By forcing or requiring coding languages, especially too early in an academic career, you make statistics seem like elitist endeavor. You'll get students to leave the program or not think a stats minor is worth pursuing. 

Interesting thoughts...

Let's go way back to the advent of the calculator. Why did we need to learn how to add and multiply if a calculator could do it for us? Yet here we are in 2024 and students in grammar school are still learning those tables. You would be aghast if you went to a tax professional and they didn't use a computer or calculator - same with a grocery store clerk. Yet, we still teach, memorize, and have competitions on who can compute the fastest.  Why? (and why Algebra 2? taking cubed roots and n-th roots... unless you are going into physics, astrophysics, and various similar enterprises, have you ever needed the n-th root of anything in practice? or even needed to understand that concept?)

Before we go suggesting turning the educational world on its head (because AI is the latest and greatest thing since sliced bread)...  let's think about what and why we teach what we teach - and the audience...

Some students - absolutely, teach them "stats for consumers." Those students will likely never go on to "do" statistics, although understanding what they mean and what they can and can't do is great. Do they need to actually calculate anything? JMP or whatever program the teacher uses might be great to demonstrate what is going on - e.g. how a histogram can "change shape" depending on the number of bins, or how the mean can change with outliers, etc).  For younger people who may actually go into statistics as a career?? That class may turn them off. (personally, I loved coming from mathematics - understanding the integration and formulae - but, I'm weird).  Younger minds are so much more tech savvy than I was (am) - so many learn to code when they are in grammar school - for those kids, coding may be the thing that attracts them to the field. (AP stats using a TI calculator, can we twinkle think on that?!). I was sad to see in many high schools that "statistics" is the easy course to take as a senior for kids who don't like math (rather than, Pre Calc in some cases). We are losing so many potential statisticians to this mindset.  

Just thoughts for a mid-day Friday... 

When I teach intro to stats classes, I use SPSS or Excel. I see absolutely NO need to use R, Python, SAS, etc. Teaching programming at that point is pretty pointless. It takes away time that SHOULD be spent learning topics or reinforcing ideas that were already taught. If someone wants to teach programming as an additional lab, great. Integrating it into the class, no thank you. 

Having taken many stats classes where the profs insisted that we had to learn how to code, they wasted many hours doing so and usually made mistakes along the way and spent a lot of time trying to figure out what went wrong. In every case I can think of, the "special package" we "need" to use in R and according to the profs, ONLY found in R, is available in every other no code stats software for the last 3-5 versions. 

As a statistician, I have NEVER encountered a data set where I needed some package or routine that ONLY exists in R, Python, SAS, etc. SPSS or JMP work really well for everything I have ever done. I find some of the methods in Design Expert to be quite useful too. In fact, the optimization portion of Design Expert does something that optimization algorithms in R and Python wont do and was the topic of 2 presentations I've given to local ASA groups. 

For giving presentations, those where a stakeholders ask for "on the fly" analyses of the data you are presenting, SPSS or JMP are far superior to R, Python. In fact, a presentation I gave to a VP at a Fortune 500 company,  after going over the analysis I did with JMP, and answered all the questions he had then and there with JMP, he went back to the statisticians and yelled at them FOR using R. 

By forcing or requiring coding languages, especially too early in an academic career, you make statistics seem like elitist endeavor. You'll get students to leave the program or not think a stats minor is worth pursuing. 

I concur with Dr. Kwasny's comments. IMHO we learn the underlying methods because that gives us a way to develop a "feel" for whether our results are "reasonable." Without going through the calculations myself and learning what is a reasonable result, I could not have developed the judgment needed to tell me when "something is wrong with this picture." 

Regarding coding ... it is a valuable skill simply because it teaches problem-solving in a structured manner. It requires looking at the problem in a way that helps you identify the critical variables and the steps needed to work out an answer. It is true that "coding may not be for everyone." But, by the same token, logic, reasoning, and disciplined thought may not be for everyone.

Interesting thoughts...

Let's go way back to the advent of the calculator. Why did we need to learn how to add and multiply if a calculator could do it for us? Yet here we are in 2024 and students in grammar school are still learning those tables. You would be aghast if you went to a tax professional and they didn't use a computer or calculator - same with a grocery store clerk. Yet, we still teach, memorize, and have competitions on who can compute the fastest.  Why? (and why Algebra 2? taking cubed roots and n-th roots... unless you are going into physics, astrophysics, and various similar enterprises, have you ever needed the n-th root of anything in practice? or even needed to understand that concept?)

Before we go suggesting turning the educational world on its head (because AI is the latest and greatest thing since sliced bread)...  let's think about what and why we teach what we teach - and the audience...

Some students - absolutely, teach them "stats for consumers." Those students will likely never go on to "do" statistics, although understanding what they mean and what they can and can't do is great. Do they need to actually calculate anything? JMP or whatever program the teacher uses might be great to demonstrate what is going on - e.g. how a histogram can "change shape" depending on the number of bins, or how the mean can change with outliers, etc).  For younger people who may actually go into statistics as a career?? That class may turn them off. (personally, I loved coming from mathematics - understanding the integration and formulae - but, I'm weird).  Younger minds are so much more tech savvy than I was (am) - so many learn to code when they are in grammar school - for those kids, coding may be the thing that attracts them to the field. (AP stats using a TI calculator, can we twinkle think on that?!). I was sad to see in many high schools that "statistics" is the easy course to take as a senior for kids who don't like math (rather than, Pre Calc in some cases). We are losing so many potential statisticians to this mindset.  

Just thoughts for a mid-day Friday... 

When I teach intro to stats classes, I use SPSS or Excel. I see absolutely NO need to use R, Python, SAS, etc. Teaching programming at that point is pretty pointless. It takes away time that SHOULD be spent learning topics or reinforcing ideas that were already taught. If someone wants to teach programming as an additional lab, great. Integrating it into the class, no thank you. 

Having taken many stats classes where the profs insisted that we had to learn how to code, they wasted many hours doing so and usually made mistakes along the way and spent a lot of time trying to figure out what went wrong. In every case I can think of, the "special package" we "need" to use in R and according to the profs, ONLY found in R, is available in every other no code stats software for the last 3-5 versions. 

As a statistician, I have NEVER encountered a data set where I needed some package or routine that ONLY exists in R, Python, SAS, etc. SPSS or JMP work really well for everything I have ever done. I find some of the methods in Design Expert to be quite useful too. In fact, the optimization portion of Design Expert does something that optimization algorithms in R and Python wont do and was the topic of 2 presentations I've given to local ASA groups. 

For giving presentations, those where a stakeholders ask for "on the fly" analyses of the data you are presenting, SPSS or JMP are far superior to R, Python. In fact, a presentation I gave to a VP at a Fortune 500 company,  after going over the analysis I did with JMP, and answered all the questions he had then and there with JMP, he went back to the statisticians and yelled at them FOR using R. 

By forcing or requiring coding languages, especially too early in an academic career, you make statistics seem like elitist endeavor. You'll get students to leave the program or not think a stats minor is worth pursuing. 

I spend an entire lecture in my stats classes discussing how to make the different types of plots. We also go into why we shouldn't trust them if we don't know where the data comes from. Nor, how the data was made/collected. A homework problem looks at data sets I made up where the plots look very similar. But, the BTS story is very different. It's also a discussion about the proper choice of metrics. 

Last week we discussed data from the Flint Water Crisis with my students from Flint. I made a histogram of the raw data. There was a data point from the original data set that was "an outlier". 95% of the data was between 0ppb and 30ish ppb. there were 2 data points at 44ppb and 1 data point at 104ppb. That outlier was removed from the data set because it was too far beyond reasonable. Sadly, that opinion extended the poisoning of the people of Flint for another 12 months or so. 

I simulated data between 0.000 and 0.499ppb for the data points that had a reported 0ppb concentration, then took the log of those data points and showed that the data was actually from a log normal distribution. Once you make that change, you see that the outlier, really does belong.

If I remember correctly, it was far from being the largest sample collected... once they knew what was going on and collected more samples. 

I concur with Dr. Kwasny's comments. IMHO we learn the underlying methods because that gives us a way to develop a "feel" for whether our results are "reasonable." Without going through the calculations myself and learning what is a reasonable result, I could not have developed the judgment needed to tell me when "something is wrong with this picture." 

Regarding coding ... it is a valuable skill simply because it teaches problem-solving in a structured manner. It requires looking at the problem in a way that helps you identify the critical variables and the steps needed to work out an answer. It is true that "coding may not be for everyone." But, by the same token, logic, reasoning, and disciplined thought may not be for everyone.

Interesting thoughts...

Let's go way back to the advent of the calculator. Why did we need to learn how to add and multiply if a calculator could do it for us? Yet here we are in 2024 and students in grammar school are still learning those tables. You would be aghast if you went to a tax professional and they didn't use a computer or calculator - same with a grocery store clerk. Yet, we still teach, memorize, and have competitions on who can compute the fastest.  Why? (and why Algebra 2? taking cubed roots and n-th roots... unless you are going into physics, astrophysics, and various similar enterprises, have you ever needed the n-th root of anything in practice? or even needed to understand that concept?)

Before we go suggesting turning the educational world on its head (because AI is the latest and greatest thing since sliced bread)...  let's think about what and why we teach what we teach - and the audience...

Some students - absolutely, teach them "stats for consumers." Those students will likely never go on to "do" statistics, although understanding what they mean and what they can and can't do is great. Do they need to actually calculate anything? JMP or whatever program the teacher uses might be great to demonstrate what is going on - e.g. how a histogram can "change shape" depending on the number of bins, or how the mean can change with outliers, etc).  For younger people who may actually go into statistics as a career?? That class may turn them off. (personally, I loved coming from mathematics - understanding the integration and formulae - but, I'm weird).  Younger minds are so much more tech savvy than I was (am) - so many learn to code when they are in grammar school - for those kids, coding may be the thing that attracts them to the field. (AP stats using a TI calculator, can we twinkle think on that?!). I was sad to see in many high schools that "statistics" is the easy course to take as a senior for kids who don't like math (rather than, Pre Calc in some cases). We are losing so many potential statisticians to this mindset.  

Just thoughts for a mid-day Friday... 

When I teach intro to stats classes, I use SPSS or Excel. I see absolutely NO need to use R, Python, SAS, etc. Teaching programming at that point is pretty pointless. It takes away time that SHOULD be spent learning topics or reinforcing ideas that were already taught. If someone wants to teach programming as an additional lab, great. Integrating it into the class, no thank you. 

Having taken many stats classes where the profs insisted that we had to learn how to code, they wasted many hours doing so and usually made mistakes along the way and spent a lot of time trying to figure out what went wrong. In every case I can think of, the "special package" we "need" to use in R and according to the profs, ONLY found in R, is available in every other no code stats software for the last 3-5 versions. 

As a statistician, I have NEVER encountered a data set where I needed some package or routine that ONLY exists in R, Python, SAS, etc. SPSS or JMP work really well for everything I have ever done. I find some of the methods in Design Expert to be quite useful too. In fact, the optimization portion of Design Expert does something that optimization algorithms in R and Python wont do and was the topic of 2 presentations I've given to local ASA groups. 

For giving presentations, those where a stakeholders ask for "on the fly" analyses of the data you are presenting, SPSS or JMP are far superior to R, Python. In fact, a presentation I gave to a VP at a Fortune 500 company,  after going over the analysis I did with JMP, and answered all the questions he had then and there with JMP, he went back to the statisticians and yelled at them FOR using R. 

By forcing or requiring coding languages, especially too early in an academic career, you make statistics seem like elitist endeavor. You'll get students to leave the program or not think a stats minor is worth pursuing. 

I agree with a theme I find in this thread - let's not be passive about the inevitability of using AI in our math and stats teaching and work, and similarly, choose the software tool that works best for the level and purpose of the course. I work in non-math/stats/data sci departments; we use SPSS for Stats I and II and for graduate research for the reasons already mentioned - and also because it's  table/spreadsheet data model is more intuitive  than R's vector model.  But for students in data science, learning more than one language, including R may make more sense.  Recently tutoring middle school students, the new pedagogy allows time to solve problems imaginatively and describing one's findings, or working in groups to solve a problem, there is less emphasis on speed and efficiency that the latest technology may impart. Working problems by hand can allow for teaching of the rich and diverse history of mathematics (e.g., Aryabhata triangle numbers...shout out to Algebra II!). Of course, again, the calculator, computer are really important tools and should be incorporated - but not de facto, but as needed and necessary. 

I spend an entire lecture in my stats classes discussing how to make the different types of plots. We also go into why we shouldn't trust them if we don't know where the data comes from. Nor, how the data was made/collected. A homework problem looks at data sets I made up where the plots look very similar. But, the BTS story is very different. It's also a discussion about the proper choice of metrics. 

Last week we discussed data from the Flint Water Crisis with my students from Flint. I made a histogram of the raw data. There was a data point from the original data set that was "an outlier". 95% of the data was between 0ppb and 30ish ppb. there were 2 data points at 44ppb and 1 data point at 104ppb. That outlier was removed from the data set because it was too far beyond reasonable. Sadly, that opinion extended the poisoning of the people of Flint for another 12 months or so. 

I simulated data between 0.000 and 0.499ppb for the data points that had a reported 0ppb concentration, then took the log of those data points and showed that the data was actually from a log normal distribution. Once you make that change, you see that the outlier, really does belong.

If I remember correctly, it was far from being the largest sample collected... once they knew what was going on and collected more samples. 

I concur with Dr. Kwasny's comments. IMHO we learn the underlying methods because that gives us a way to develop a "feel" for whether our results are "reasonable." Without going through the calculations myself and learning what is a reasonable result, I could not have developed the judgment needed to tell me when "something is wrong with this picture." 

Regarding coding ... it is a valuable skill simply because it teaches problem-solving in a structured manner. It requires looking at the problem in a way that helps you identify the critical variables and the steps needed to work out an answer. It is true that "coding may not be for everyone." But, by the same token, logic, reasoning, and disciplined thought may not be for everyone.

Interesting thoughts...

Let's go way back to the advent of the calculator. Why did we need to learn how to add and multiply if a calculator could do it for us? Yet here we are in 2024 and students in grammar school are still learning those tables. You would be aghast if you went to a tax professional and they didn't use a computer or calculator - same with a grocery store clerk. Yet, we still teach, memorize, and have competitions on who can compute the fastest.  Why? (and why Algebra 2? taking cubed roots and n-th roots... unless you are going into physics, astrophysics, and various similar enterprises, have you ever needed the n-th root of anything in practice? or even needed to understand that concept?)

Before we go suggesting turning the educational world on its head (because AI is the latest and greatest thing since sliced bread)...  let's think about what and why we teach what we teach - and the audience...

Some students - absolutely, teach them "stats for consumers." Those students will likely never go on to "do" statistics, although understanding what they mean and what they can and can't do is great. Do they need to actually calculate anything? JMP or whatever program the teacher uses might be great to demonstrate what is going on - e.g. how a histogram can "change shape" depending on the number of bins, or how the mean can change with outliers, etc).  For younger people who may actually go into statistics as a career?? That class may turn them off. (personally, I loved coming from mathematics - understanding the integration and formulae - but, I'm weird).  Younger minds are so much more tech savvy than I was (am) - so many learn to code when they are in grammar school - for those kids, coding may be the thing that attracts them to the field. (AP stats using a TI calculator, can we twinkle think on that?!). I was sad to see in many high schools that "statistics" is the easy course to take as a senior for kids who don't like math (rather than, Pre Calc in some cases). We are losing so many potential statisticians to this mindset.  

Just thoughts for a mid-day Friday... 

When I teach intro to stats classes, I use SPSS or Excel. I see absolutely NO need to use R, Python, SAS, etc. Teaching programming at that point is pretty pointless. It takes away time that SHOULD be spent learning topics or reinforcing ideas that were already taught. If someone wants to teach programming as an additional lab, great. Integrating it into the class, no thank you. 

Having taken many stats classes where the profs insisted that we had to learn how to code, they wasted many hours doing so and usually made mistakes along the way and spent a lot of time trying to figure out what went wrong. In every case I can think of, the "special package" we "need" to use in R and according to the profs, ONLY found in R, is available in every other no code stats software for the last 3-5 versions. 

As a statistician, I have NEVER encountered a data set where I needed some package or routine that ONLY exists in R, Python, SAS, etc. SPSS or JMP work really well for everything I have ever done. I find some of the methods in Design Expert to be quite useful too. In fact, the optimization portion of Design Expert does something that optimization algorithms in R and Python wont do and was the topic of 2 presentations I've given to local ASA groups. 

For giving presentations, those where a stakeholders ask for "on the fly" analyses of the data you are presenting, SPSS or JMP are far superior to R, Python. In fact, a presentation I gave to a VP at a Fortune 500 company,  after going over the analysis I did with JMP, and answered all the questions he had then and there with JMP, he went back to the statisticians and yelled at them FOR using R. 

By forcing or requiring coding languages, especially too early in an academic career, you make statistics seem like elitist endeavor. You'll get students to leave the program or not think a stats minor is worth pursuing.