Ethics – Part 4
Scientology 1.0.0 – Chapter 37
In almost every case where I’ve been asked to help resolve administrative trouble — whether for a group or an individual — I’ve found that statistics were poorly maintained, if they were kept at all, and that the Ethics Conditions and their formulas were either misapplied or ignored altogether. In my experience, keeping accurate statistics and using the Conditions often comes with baggage: people have been made to use them arbitrarily in the past, or they’ve decided these tools are too complicated compared with the carefree administrative approach of simply ‘winging it’.
Despite the risk of testing the readers’ patience, it’s crucial to cover statistics before moving on to possibly more electrifying matters, particularly because how they are used has had and has a massively significant impact on both the evolution of the Church and the ethos of its current operations.
Statistics
A statistic is a numerical measure that describes something — for example, a count, a rate, a percentage, or an average. When you record the same statistic repeatedly (often at regular intervals) and plot it on a graph — typically with time on the horizontal axis — you get a series of points that shows how the measure changes. A trend is the overall direction or longer-term pattern in that series (upward, downward, or broadly flat), distinct from short-term noise or one-off spikes. Trends are often judged by looking at the general slope of the plotted series. ‘Yes, yes (snore),’ one might say. ‘So what?’
What isn’t always so well known in practice is that trends turn numbers into Conditions. In other words, it’s the trend that determines the Condition.
I have seen many people struggling away with a formula for weeks, even months, and I’ve witnessed a few cases where the formula for the Condition of Liability was applied even longer (if you could call it application), all because there were no graphs, no visible trends. Or, if there were, they were ignored. (A trend exists whether you plot it or not.) In practice, the Condition isn’t a mood; it’s read off the trend. Without a graph, one might get stuck grinding away on a formula to solve all of life’s problems in order to achieve a utopian version of Normal Operation — and risk ending up in a Condition of… well, Confusion. Possibly such grinders believe Normal Operation is a Condition in which there are no problems; I don’t know.
It is a fact, however, that these formulas can be applied and executed rapidly, particularly when used at the level of individual postulates and action. For instance, the sudden need to change one’s life that can happen with some people who’ve been trending downwards (‘hitting bottom’) is a very rapid switch out of a truly deep Confusion indeed. To see a rising trend reliably on a graph, however, may take longer. In practice, you don’t get a trend from ‘two numbers’; you get it by tracking the same measure across enough points to distinguish a genuine rise or fall from random variation. A formula can be executed in minutes; a trend can take weeks — and, when it finally shows, it validates (or falsifies) one’s Condition and corresponding formula.
But when you do finally have an uptrend, ignoring it might cause even bigger problems. If you keep hammering the Danger formula when the stat is already rising, you destabilise recovery.
For instance, violent crime (including assault and homicide) in the United States began rising in 1960, peaking in the early 1990s. Then, suddenly, violent crime began to drop, continuing to fall for two decades. This phenomenon was particularly intriguing because the news and social media during this miraculous and happy downtrend were implying the opposite: if you only read headlines, you’d think it was open season. This failure to recognise an improving trend (and to reinforce what was working) may have helped create the conditions for the unfortunate increase in violent crime we saw in the mid-2010s. Imagine how widely publicising the actual statistics might have fostered an investigation to discover whatever successful actions were causing the trend, thereby influencing public policy to reinforce such outbreaks of relative peace.
Fortunately, according to 2024 measurements, violent crime is decreasing again, but a single agreed-upon causal story explaining why is still lacking, at least based on what I’ve seen. However, judging by mainstream media coverage, one might be forgiven for believing that violence in the United States is at an all-time high, which it ain’t. Not by far.
Anyway, statistics like these — and many others — are vital in assessing Conditions and thereby figuring out which formulas to apply.
Graphs
Graphs are the simplest tool for keeping your Condition honest because they make trends legible before emotions, anecdotes, ideology, and politics take over.
Finding the right Condition is often straightforward. Although choosing the right measurement and time frame is a bit of an art, it can be done for almost anything, including financial performance, customer satisfaction, or organisational efficiency, but once done, there’s your trend and corresponding Condition.
Where expansion is the primary success indicator, decide what your measurements are that show expansion and put them on a graph. Once you’ve got your graph, start plotting the statistic to produce a trend. When you have a trend, a flat line reads as Emergency; a sharp sustained drop reads as Danger (or worse, if below the Normal Operation band); a slow steady rise reads as Normal; and a sharp rise reads as Affluence. And the Affluence formula, if applied correctly, ought to take you into a higher Normal. Done right, the trend tells you the Condition and thus the appropriate formula to apply. All this is covered in the book Introduction to Scientology Ethics.
For instance, you sell widgets. Selling one or more additional widgets each month would give you a rising trend and place you in Normal Operation. Eventually, as your sales increase beyond your costs, you can then invest in expansion and even create reserves. However, even a profitable flat stat reads as Emergency, because it isn’t expanding and will eventually be outflanked by other factors. Thus the objective would be to increase your sales gradually each month to cultivate a thriving business; you get the idea.
If the operation were one where a steady mean equals success (a flat trend), then it would be graphed differently. For example, if your healthy weight is 112 pounds, being at 112 would be Normal Operation. If the target is a stable set-point, Normal is the band around the set-point; movement away is decline. In other words, weights above or below 112 would indicate Emergency, etc. Combined with another graph showing steady improvements in some sort of exercise (e.g., resting heart rate, weekly miles, weights lifted), you’d have a pretty good picture of a healthy lifestyle.
Furthermore, using the right time frame is crucial; some things can be measured in nanoseconds (stock trades), while others can be measured by the decade (population). It would create stress and havoc if you tried to measure every important metric by the week, as this could lead to misinterpretations of trends and performance, particularly in fields where longer time frames provide a clearer picture of success. As an example, a successful real estate agent might sell 10–20 properties a year, so they should use a quarterly and annual reporting period. Selling just one additional property a year might show either Normal or Affluence, depending. Assigning Conditions based on a weekly graph in such a case would render the resulting formulas meaningless. If the time frame is too long or too short, the trends either become noise or produce no signal at all.
Such graphs can be vital because, along with other indicators, they help determine which programmes to implement and which to avoid. Additionally, it is crucial to identify a slowly developing trend that may go unnoticed until a drastic event occurs. Cases where things are actually going along well and then tragedy suddenly strikes are often rarer than they look in hindsight. More often, things have been getting gradually more dangerous — such as sliding into debt — so that when an emergency does inevitably occur, it’s far worse than it might otherwise have been. An example of ignoring certain signs overlong was Hurricane Katrina, where decades of state and local administrative ‘dysfunction’ tragically ‘delayed’ plans to build sea walls. Such small deviations might create consequent emergencies, as is so often the case with health.
Other instances have resulted in trouble because the individual, or the group under management, failed to notice things were getting better and persisted in applying too low a Condition. For example, very often I have seen people applying the Danger formula long after they should have been applying Normal Operation. Applying the formula for a Condition that is too low can be just as serious a mistake as applying it for a Condition that is too high.
As an aside — and I feel I ought to mention this because I have seen this sort of thing so many times — certain people appear to fetishise Emergency because it produces lovely amounts of noisy activity, exactly the sort of thing that looks good if your perceived ‘stat’ is motion rather than outcome. Certainly, over-applying a low Condition keeps people in ‘emergency mode’, but with costs such as stress, burnout, breakdowns in cooperation, and unnecessarily severe executive bypass. I reckon it’s this sort of thinking that can goad groups into endless rounds of Dangers and Emergencies, because in those Conditions some people feel like they’re ‘really doing something!’ Whereas Normal Operation, being as relaxed as it is, feels like loafing to anyone stuck below 2.0.
To be fair — and I’ll get on with statistics in a minute — it seems Western culture has become locked into a stress mindset. Regularly failing to notice improvement trends and operating in the Emergency/Danger feedback loop (actually a Condition of Confusion) seems to have become the ‘new normal’. I’d guess it might just be a case of too much noise to notice the correct signal. I don’t know; just saying.
Anyway, to continue: another useful tool is the ‘upside down’ graph. Consider the example of going from being in debt (the red) to achieving savings (the black). Invert the vertical axis so that less debt moves upward. As you work to get out of debt, you will notice a positive trend. If the trend is up, that’s Normal Operation, even if you’re still in debt. Then, when you’re out of debt, switch to a savings stat and plot that upward.
In this framework, the Condition determines the formula, and the formula determines the next actions.
Wrong stats
A perfect trend based on a useless metric is still useless.
The statistics we concentrate on in Western culture quite often don’t measure the social value of the ‘product’ or ‘service’. Instead, we tend to obsess over the all-but-sacred gross income (GI) statistic, which measures the total income generated by a business. Drug cartels prioritise GI too, so you might see the problem. GI mainly indicates financial viability; it says little, or nothing, about value (moral viability).
When a government institutes a new programme or enacts new laws, the public seldom learns from graphs whether these changes are working. Often this is because the policy or law lacks a provision to measure the programme’s success or failure and publish the results publicly. Usually, though, what gets defended and celebrated is increased budget, headcount, and busyness — not necessarily outcomes. This tendency is one of the reasons bureaucracies rarely shrink without being forced to do so; they grow bigger and bigger until they become wholly involved in their own perpetuation at public expense. The pattern is easy to see when administration grossly outstrips delivery: in some university central administration offices, there are roughly 3–5 administrators for every one technician or service staff member; in some government regulatory agencies’ central offices, roughly 4–8 administrators for every one field technician or inspector. Actually, bloat poses a risk for any group but is especially endemic to organisations whose financial support comes from donations, is coerced (taxes), or is a hidden tax (inflation), and especially in cases where their accounting practices are nearly invisible (government, ‘NGOs’, etc.). Apparently, it’s easier to be an ‘administrator’ than being a person who needs actual training to do any actually productive work (God bless the middleman).
Another example is the media, which primarily aim to attract attention: ‘eyes on’ = ad revenue. It’s easier to hit that stat by tickling the fear-and-hostility bits of the limbic system, keeping viewers glued to their screens. Using these sorts of tactics to increase viewership and interaction (as is the case with social media) could very well be a contributing factor to a decrease in public morale and rise in mental illness. I mean, when stuck attention is the stat, outrage can be the product.
The same sort of thing happens with medicine. More sick people means more medicines dispensed and more medical procedures = more money. The revenue model rewards volume over health.
Or food. The more people consume, the higher the profits. Then they get so fat they have to go see a doctor and become a source of income for the medical field. I mean, consider how the tobacco companies R.J. Reynolds and Philip Morris, famous for making their products more addictive, bought up such major food companies as General Foods, Nabisco, and Kraft Foods. Maybe Big Pharma owns the tobacco companies; I’m just kidding around, but… who knows? Worth looking into.
Or litigation. It costs more and more to go to court partly because law firms measure their bottom line to evaluate success. The result? Case law ‘evolves’, precedent stacks on precedent, and the whole thing becomes even more specialised (read: needlessly complicated)… that, of course, requires the ‘experts’ (lawyers) to translate the whole sorry mess for the rest of us. Who, by the way, are now so fat and sick that we have to hire them to sue Big Pharma and General Foods to pay for our pills, shots, and bariatric scooters.
So, the lesson is: measure the wrong thing for reward, and you’ll get it.
Fake stats
Actually, it gets worse. Sometimes the metric isn’t merely useless — it’s distorted.
The previous examples are simply measuring gross and net income, which are, at least, real. There are other statistics being reported that affect our lives, but they measure constructs that are partly artefacts of poor definition. Using such false statistics can lead to strange outcomes.
For instance, if the central bank expands the currency, you can make nominal totals look grand while real purchasing power quietly slides off. It might look like people are ‘making’ more ‘money’, but not necessarily. Today, on paper, the U.S. is richer than ever! The NASDAQ has never traded higher, but has the middle class expanded? Can most people still buy their own home by the age of 30? You see how silly all this is?
Then there is employment: in the early 2010s, the unemployment rate in the U.S. was around 8.1%. Today it is something like 4.4% — hooray! But… in the official statistics, a person is counted as unemployed if they are without a job, available for work, and have actively sought employment within the prior 4 weeks (i.e., in the most recent 4-week period). But lo, if they have not searched in the past 4 weeks, they are not counted as unemployed; instead, they may be classified as ‘not in the labour force’, and – if they want a job but stopped searching – they may be counted as ‘marginally attached’ or a ‘discouraged worker’, depending on the reasons for stopping.
This means headline unemployment can fall because people find jobs — or because they stop looking and vanish into ‘not in the labour force’. If you do not also monitor participation rates and broader measures, you may mistakenly congratulate yourself on a statistical mirage. Today, who knows how many people are actually unemployed? But it’s down! (Maybe.)
And don’t get me started on GDP. Gross Domestic Product is the total monetary value of all final goods and services produced within a country during a specific time and is often used to gauge a nation’s ‘success’. Okay, fine. But GDP is a rather limited proxy for a country’s actual well-being. It’s tallied through spending categories (consumption, government spending, investment), which can obscure what’s actually improving — and it can rise for reasons that don’t feel like progress. It also ignores the non-market value of things that do increase well-being, such as unpaid parenting and household labour. That is: mothers raising the next generation; without which there ain’t no GDP, or anything else.
Upshot
Choose metrics that track survival and value. To raise the Condition, one must determine a desirable outcome that benefits both oneself and society: the approach that maximises survival for as many dynamics as possible.
Work to ensure your graphs are resistant to manipulation.
Always select an appropriate time frame and avoid truncating it until motion replaces value. In other words, avoid using a very short time frame, like one week, to measure something that operates over longer intervals, as this will result in graphs that lead you to apply Conditions that are too low, causing unnecessary stress and making you run around like a headless chicken.
Consider measuring factors beyond gross income, such as cash flow basics (bills versus income), customer indicators, visibility, public relations, goodwill, etc.
And don’t stuff ’em in some drawer; put them where you can see them.
Even with statistics, be sceptical. Without them, though, be much more doubtful.
Coda
Of course, as usual, there’s a lot I’ve left out, as those familiar with the subject of statistics in general and Scientology administrative policy in particular will know. I just wanted to lay out a little about the subject now because it bears on factors I’ll be discussing later; possibly, though, what little I’ve said might be useful to some readers.
But the fact remains, we are not a society that operates on statistics, yet year after year we go to the polls and vote on issues that we can’t possibly know will work and support candidates who are confident that we will never scrutinise their statistics to determine whether any of their legislation is actually effective. And, anyway, many of us don’t know a thing about data evaluation, in which statistics play a primary role. We just don’t bother with any of it, not even in our private lives.
But why not? What’s the reason statistics are ignored, skewed, faked, not reported, or simply not kept (but mainly just ignored)? Possibly it’s because the whole subject is boring (‘Why did Arthur have to drone on about statistics?’ ‘That’s another eighteen minutes I’ll never get back!’). Or maybe it’s because data evaluation, which often uses statistical information, has become a specialised field. In my opinion, data evaluation should be a main study.
The question is: are we truly engaging in critical thinking and taking action on the issues that deserve our attention? I don’t think so. For instance, I’ve noticed that even when people point out the importance of data evaluation and statistical information for making day-to-day decisions — particularly regarding social issues — the typical response is crickets.
One example of this was during the lockdowns. Within the first two weeks I noticed what looked like a massive anomaly in the way U.S. death figures were being compared with other countries’. Why did people in the U.S. seem to be dying like flies? So I started digging.
And almost immediately the ‘anomaly’ started shrinking — because the comparison was a mess. People were mixing raw totals with per-capita rates, ignoring reporting lags, comparing different age profiles, and treating shifting definitions as though they were laws of physics. Once you do the boring work (denominators, time windows, basic stratification), a lot of the ‘scary’ just plain evaporates.
What remained — what I could actually use — was the signal: the risk was not evenly distributed. For most healthy people it wasn’t the universal Russian roulette the headlines implied. That understanding made me much calmer than many people I knew, because I could finally see the Condition we were actually in, rather than the one I was being sold.
So. In my opinion, we are not thinking critically as much as we could be. Not as a society, anyway. The data is always out there; getting it is no problem if one bothers. However, even if one bothers, accurately evaluating the data in a useful way still seems to present a significant challenge for too many people.
What to do, what to do.
I suppose the best course of action would be to develop a programme that helps individuals become capable of better thinking. This improved thinking would involve using existing tools, such as actual statistics, which means being able to identify when these statistics are false, missing, or measuring the wrong things.
If enough people could see through the fog of confused data — or notice when it is missing — they could evaluate it, judge accurately, and design better ways forward.
And be a lot calmer.
Arthur Ronald Conway Hubbard 