I am 50% Smarter Now that I Know “How to Read Numbers”
I know what you are all thinking about my title: “Scott, without giving us your IQ number, we can’t determine how much smarter you have actually become.” Fair point, and one that you might not have identified as deficient quite as quickly if you hadn’t engaged with this intriguing book.
Many of us are familiar with some kind of phrase expressing a degree skepticism around statistics. The one most familiar to me is, “You can get statistics to say anything” and in some respects Chivers and Chivers book affirms this reality. Perhaps more specific, they would assert that lots of statistics don’t tell us anything of value because of a flawed process, dishonest intent, bias or lack of information to give context to the data. Thankfully, the authors help novice readers of statistics (me!) by providing some basic guidelines to ensure good data collection and presentation. They have also created some further work for me as it relates to my NPO.
As I was articulating and ‘testing’ my NPO with stakeholders and my one-on-one interviews, I was aware that my NPO statement had an unconfirmed assumption in it. In fact, it’s the very first word of my NPO statement: “Many”
“Many Christian leaders in the Canadian C&MA are…” (not prepared for ministry leadership)
I knew that I would have to quantify that statement somehow. First, I would have to get access to the necessary data (ie. The statistics), which I felt was feasible. The more difficult aspect would be interpreting the data to quantify and validate the use of the term, “Many.”
“Many” according to what? Or according to who?
Of course, we can’t expect 100% of Christian leaders to be sufficiently trained/prepared for leadership—there will inevitably and always be some who, for a variety of reasons, are not ready for the task. So while we all might wish every leader was adequately prepared for their role, we are all also realistic enough to allow for a certain percentage to miss the mark. But what is the ‘acceptable percentage’ of unprepared leaders? How many is too many?
Implicit in my NPO is my own bias that there are currently “too many” unprepared leaders in my own tribe—thus my naming it as a problem to be solved. But how will that be quantified and proven? In some respects, this is a subjective assessment—some might be comfortable with 10% of Pastoral leadership being unprepared for the task while another might be fine with that number being 20%. Another might dogmatically claim that for the sake of Christ and His church we can’t be satisfied with more than 1% of our Pastoral leaders being unprepared. Who is right?
In this respect, even the statistics gathered using best practices are then open to interpretation as various people define their own ‘acceptable risk’.
Speaking of best practices: Chivers and Chivers’ website gives a great summary of doing statistics well: “The Statistical Style Guide” (2). This ‘cheat sheet’ will be a very helpful resource for me moving forward—ensuring I am gathering and communicating statistics related to my NPO in effective and accurate ways.
One point stood out to me as I worked my way through their 11 point summary: #7. Be Careful about saying or implying that something causes something else (3). As I explore statistics related to pastoral burnout, resignations, and poor choices that disqualify them from ministry, it can be tempting to be overly simplistic and say, “Lack of Training is the reason”. Yet the truth of the matter is, there might be a correlation and there might not be. More often than not, there are multiple causes—some of them obvious and some of them less so—that result in a particular outcome, and this seventh point is a great reminder to not let personal assumptions or bias result in over-simplification and ultimately inaccurate reading of the data.
While I am glad for this resource (and in an effort at full disclosure), I also find myself slightly overwhelmed at the task before me. Engaging in statistical work is not my forte—I am a pastor, after all, and we are a notoriously “number-challenged” lot!
“How to Read Numbers” has provided me with a helpful roadmap as I anticipate the statistical research related to my NPO, but I also feel like the book made ‘driving the car’ more complicated and I may need to take some further ‘driving lessons’ to successfully navigate the journey!
- Chivers, Tom, and David Chivers. How to Read Numbers: A Guide to Statistics in the News (and Knowing When to Trust Them), 2021.
- https://www.howtoreadnumbers.com/stats-style-guide
- https://www.howtoreadnumbers.com/stats-style-guide
About the Author
Scott Dickie
10 responses to “I am 50% Smarter Now that I Know “How to Read Numbers””
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Hi Scott, thanks for end note #2. Like a dutiful doctoral student, I have asked Zotero to save it in my statistics folder for my Immigration NPO. I will now look 50% more intelligent.
So many numbers to plow through, and I am rereading Biased Samples (p. 19 and Statistical Significance p. 35. The political football called immigration provides ample evidence of both bias and poor statistical significance.
As the US presidential election gears up. I am sure that AMAZING statistics will be ripe for my NPO picking (a little like cherry picking (p.113) – but I would NEVER do that!)
I am one those people who actually reads the customer reviews on Amazon.com purchases. So by the books standards – I should call myself Mr. Anecdotal? Sigh….Thanks for you comments…Shalom…Russ
Slightly overwhelmed? I am 100% overwhelmed!
I appreciate your question, “Who is right?” when it comes to subjective assessment. I began my NPO with “There are not enough mental health resources in Bend” (or something along those lines). I was quickly corrected by a big-wig in the mental health field in Bend telling me that there are in fact, “plenty of resources” they are just not well known. The past few weeks I’ve been leading a class for family members of those who live with mental illness. I am constantly hearing from them that there are not enough resources. Who is right? I don’t yet know – nor am I sure I will be able to find out.
I hear you Kally….and even if you did find out the exact number of resources and how long the waitlist is to get in….WHO gets to determine if that is acceptable or not? All of the data inevitably gets interpreted through our own assumptions, bias and subjectivity, even if the numbers are collected correctly.
The good news is, if you are arguing policy or for increased (or decreased) investment in one sector over the other…you can at least have a debate about the best way forward WITH CORRECT DATA! The conversation/debate is a giant waste of time if the statistical work hasn’t been done well. Yet in one sense, the work to get good data is just the beginning of the work–figuring out what to do with the data (and even if anything needs to be done) is an equal if not greater amount of work.
Sometimes data doesn’t represent the true cause or multiple causes. I think about this in light of Ephesians 6:12 “For our struggle is not against flesh and blood, but against the rulers, against the authorities, against the powers of this dark world and against the spiritual forces of evil in the heavenly realms.” There is so much more represented in any given situation and circumstance than what we see. Dallas Willard in his book, Life Without Lack, says “In our world people maintain their sense of respectability by rejecting everything except what they can see in the natural world. To accept that there is more than that threatens their self-identity as proper, intelligent citizens of the modern world.” There is so much more going on than what we can see.
Yes Cathy!
As I reflect on Pam’s list on her post, I think:
#1. Hasty Generalization: Inductive reasoning when too few examples are cited.
and
#3 False Cause: Causal reasoning when insufficient evidence/one thing caused another.
Are very tempting. Easy, surface answers that likely confirm our own assumptions so we fail to take into account all of the various factors leading to particular outcome. Very important to be aware of….and also humbling, because we can NEVER truly understand all of the factors that lead to an outcome! People are complex and our best efforts will only ever produce reasonable understanding, never absolute certainty.
Scott, thanks for your post and your transparency.
I had a similar NPO to start with decrying the ‘lack of Gen Z leaders’ in our church until I realized that everyone would define ‘lack’ differently (now it’s “there is a need for more”).
So maybe this is an offline question but I’m curious, now that you have solved the “many” crisis in your NPO, how will you define the “not prepared for”? (how will you statistically measure how leaders may not be not prepared?).
Hey Tim,
I’m not totally sure that I’ve figured out the “many” and I may need to adapt my NPO further as you have already done.
Lots of thinking and planning to be done as it relates to you second question: to start, I need to determine what the ‘base level’ of education/formation is required for Pastoral leadership (ie. Bible courses, self-awareness activities, mentoring, basic business, leadership, Emotional Intelligence, etc….) and then determine what gaps are in our standard training streams of Pastors in our Denomination. That’s the current fuzzy plan forward!
I appreciate how you so directly connected our reading to your NPO thinking. Your thoughts reminded me of a continual problem we encounter – missionary attrition. It’s hard not to get cynical when we see time and time again new missionaries come to the field, struggle to adapt, and leave. It’s hard to know where the problem lies. Is it our recruitment or onboarding processes? Did those individuals misinterpret their “call”? Did they just give up too quickly when they should have persevered through the hard stuff? Obviously, there isn’t any one simple answer.
You mention the need for some further “driving lessons.” Where do you think you’re going to look for a little more help in navigating statistics as you move forward?
Great stuff man! Who would have thought the word “many” would create so much work. I ran into a similar dilemma with NPO. I’m in the process of finding diverse data to support the “many” in my theory as well without biased samples. Glad to know I’m not the only one who ran into this!
You made me stop and think about my NPO! I also started with many. I think eventually I took that out and just started with the subject, but it is easy to say “many” and all those assumptions that are so easy to jump to. Thank you for bringing light to how easy it is to lead with assumptions.