Right now I am teaching an Intro to Stats class through a Math dept at a comm coll. I'm also teaching a service stats class through a university health dept. Most of the concepts are the same. But, the health dept course requires some extra material. At this university, the math and stats dept offers 4-5 different versions of algebra based intro to stats. Then, there are all the other departments that offer their versions of stats classes.
With the exception of the 2 course sequences in stats i.e. Business Stats 1 and 2, Psych Stats 1 and 2, etc, it would be really easy to cover all the different topics covered in all those different departments in one class. But, you'd need to remove pointless topics and use relevant examples. With the discussion about Med vs Med B, and which one do you actually choose, that is both a medical and business decision. For those student and departments that ask, "When are we (or my students) going to use this stuff?" They realize there is more to making a decision than "Is there a statistically significant difference?" If you can get students and departments to realize that the math dept version of a stats class is relevant to what they need in their dept, you can start to get rid of the service courses, which are usually taught by someone who uses stats a lot. Not someone that has a stats degree. So, you can start to do away with the misinterpretation of stats these service courses tend to provide. Like, when you do ANOVA, you don't need to make corrections for multiple comparisons.... (Which actually IS NOT in the basic stats textbooks I tend to use!!!) or if you have X1 to Xn, vs Y, they will do 'n' simple linear regressions or correlation analyses. instead of one multiple linear regression.
If you want a theory based class(es), most of the time that would be a Math Stats 1 and 2 course sequence.
I think it is a great service to all humanity if statisticians taught stats classes and showed how and why you do things they way we do. Unfortunately, most statisticians don't know where you would use stats. Most programs DON'T encourage students to take classes outside math and stats. If they do, it's usually a mat hand stats related course. Which means, those business case uses are a foreign idea to statisticians. (Which leads to the assumption that academic statisticians won't do well outside of academia.) True or not, doesn't really matter.
Original Message:
Sent: 03-14-2023 11:48
From: Jim Baldwin
Subject: Modernizing Intro to Stats
I am not in academia so certainly take this thought with a grain of salt (or less).
I agree that teaching students (and even especially some faculty) how to think about data collection, analysis, and interpretation in a more practical manner would be great. But are not such "statistical service courses" sanctioned by the applied departments? (Political Science, Nursing, Biology, etc.) If you remove the most of the technical aspects, would not all those departments decide that they could handle that better with their subject matter knowledge resulting in statistical service classes disappearing followed by statistics departments disappearing?
Isn't that already happening in the real world with everyone with access to R thinking they're a statistician? (I'm thinking of Rodney Dangerfield: "I don't get no respect".)
Jim
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Jim Baldwin
Retired
Original Message:
Sent: 03-14-2023 11:30
From: Willis Jensen
Subject: Modernizing Intro to Stats
I love this thought Andrew - I think Stats courses should be critical thinking courses on how to reason with data. How to make good decisions with data and recognizing the limitations of data. They should be taught more like law school classes than mathematics classes. I'm thinking of a law school case study approach where you argue and debate both sides of a conclusion and consider the evidence for or against that conclusion. To see how the data support the conclusion and the potential ways the data might not support that conclusion.
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Willis Jensen
HR Analytics
W.L. Gore & Associates
Original Message:
Sent: 03-13-2023 21:49
From: Andrew Ekstrom
Subject: Modernizing Intro to Stats
Over the last few years, the students in my classes have gone from being more 40% business, 40% nursing, 20% other to like 80% business, 20% other. For those business majors, I know they will take a "Business Stats 2" class. Everyone else, it's one shot. I know that 100% of my students are not, and never will even consider, stats or data science as a possible major or minor. So, I look at it through the lens of, "If I don't say anything, they will NEVER know. So, I best speak up."
One of the fun things I get to do with my students, because I don't waste too much time with the meaningless mechanics and pointless approximation methods, is discuss the difference between statistical significance vs practical significance and decision making with help form stats. Today, we looked at Medication A vs B. We found Med B was "Significantly different and better" and asked, " If Med A is the standard of care, should we now switch to Med B?" The textbook leads one to believe "Better is better, so switch." I threw in some other issues to think about. Like, when my G'ma had her meds switched from cheap to expensive meds. The expensive meds cost so much, she took a few per week instead of 1 per day. I asked the moral question of, "Is it better for someone to take a less effective med as prescribed or a more effective med less often than prescribed?" My argument was cheaper as prescribed is better because we know the benefit/cost. We don't know much about not taking meds as prescribed.
That type of dialogue doesn't occur in most intro to stats classes. But, that involves critical thinking about real world examples and how to use stats to answer it....
Sadly, this approach doesn't work well with standardized tests. But, id rather have students with critical thinking skills for real world problems than "The smartest class" when it comes to a dept final.
I'll take a look at your article soon. Thanks for that.
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Andrew Ekstrom
Statistician, Chemist, HPC Abuser;-)
Original Message:
Sent: 03-13-2023 09:59
From: Willis Jensen
Subject: Modernizing Intro to Stats
I can't think of any good reason - unless we are training statisticians. But in the vast majority of cases, we are not teaching people who will be statisticians. I'm with you Andrew on this one. Here's an article I wrote up on this, wondering why we continue to insist on teaching people the mechanics and steps. If we leveraged the technology and software, we could do so much more in teaching students how to be better consumers of statistical methods, how to use them appropriately. And then we wonder why there is so much mis abuse (mostly due to ignorance) of statistical methods?
https://medium.com/towards-data-science/redesigning-the-traditional-statistics-class-to-teach-data-consumers-8c057e55ddc4
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Willis Jensen
HR Analytics
W.L. Gore & Associates
Original Message:
Sent: 03-10-2023 11:08
From: Andrew Ekstrom
Subject: Modernizing Intro to Stats
Hello Everyone,
Since I've been laid off from teaching a few places, I've gone back to tutoring students in Math, Stats, Chem, Physics, etc.
I've had lots of students come in for help with their intro to stats classes. It's abundantly obvious to me that their profs were pure mathematicians and are merely going through the motions of teaching stats. I've also had to modify how I teach my stats classes to help my students get better grades with their online hw sets.
All of my students are expected to have a TI-83 or better calculator. If they don't, I have 18 I can lend out to them. I bought them with my meager salary for about $10-15 each, used. I show them how to use their calculators to the fullest extent and give them test problems were they need those skills. However, I keep having to take time away from teaching useful things to explain to my students, both in class and tutoring, that the problems they see in the online hw and on the other profs tests assume its 1923, NOT 2023.
For example, yesterday, I had to discuss how to calculate confidence intervals with 4 students. The problem that came up each time was, Part A) Find the point estimate, B) Find the Margin of Error, C) Find the xx% confidence interval. The online problems HAD to be done in that order. Each time, I had to discuss the use of the Z-table or the (woefully inadequate) T-table in the back of the book. We had to discuss how when n1 + n2 > 30 or 45 or 60, they should just use the Z-value instead of a t-value. Then, we worked on online HW problems where the answers were either answered using typical software or a TI-83/84 calculator and NOT what the textbook said. Other times, we had to use the Z approximation.
Meanwhile, their calculators allow them to answer the same questions using the various confidence interval functions. But, they'd have to answer it as: A) What is the Point Estimate? B) What is the xx% confidence interval? c) What is the margin of error? Doing the problem in this order allows students to get through problem in seconds instead of minutes. There is a lot less frustration too.
Along with those issues, there are also the normal approximation methods for the Binomial and Poisson distributions. These are basic functions in a TI-83. No need for them anymore. But, in order for my students to get the points on their online HW, I have to waste class time and say, "Sometimes we have to assume it's 1923 and this is how we will answer these questions." Which almost always brings up the question, "Does <programming language/package> solve problems this way?" Since the answer is "NO", I then have to explain why they will be tested on, or given hw problems based upon, the approximation methods, and the exact values from the calculator/software. Which adds to their frustration AND increases the amount of time they have to waste on exams and doing hw.
There are dozens more things I could complain about too. But, for now, I think this is enough.
Anyone have any ideas and factually based good reasons why we insist students assume it's the 1920's?
Thanks again!
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Andrew Ekstrom
Statistician, Chemist, HPC Abuser;-)
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