Episode 154: The Democratic Delusion

Many people seem to think that the democratic system of government extends beyond how the state is run and into civil society. In this episode, I advance the theory that this has caused a lot of people to fall prey to propaganda and misunderstand how journalistic reporting and scientific enquiry should be done.

This is a companion discussion topic for the original entry at https://privatecitizen.press/episode/154/
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Dear Fab,

Welcome back and thank you for the interesting topic, as usual.

Before jumping to the episode’s topic proper, let me say that you sold me on bouldering, I will try to find a place around here, I think it would be a fun exercise.

Now regarding the observation/theory that you are proposing, I can’t say that I have heard the same argument before, but it resonates with things that I believe in as well.

But, let me start with the topic of definitions: For me, any discussion or argument boils down to definitions. I’m not sure what is the accepted reference definition of “Democracy”.

Your point of view that Democracy refers specifically to a way to structure government (the original definition), and do not agree with the definition creep that seems to be happening.

For me personally, I would tolerate this, as long as we agree on what we are talking about.

But on the other hand, the interesting point that you raise is the reminder that consensus does not equal truth!

With which I totally agree.

We might expect that, provided some conditions, consensus would approach “Truth”. These conditions would include something like free access to all available data, and giving equal rights to participants. And from this comes the importance of a free press for democracies.

For me the sign of a good episode is how much it trigger thoughts, and for me several topics jumped to mind, but now, when writing this, I start to forget the context, to please bear with me with this apparently random thought (although I have re-listened to the episode again after writing the initial draft of this feedback, but still, I have gaps in my memory).

Speaking of Politics: there’s a card game that I like called “Illuminati”.

It’s a tongue-in-cheek model of politics, which I find quite interesting. And funny thing is that Mike Duncan (from the Revolutions Podcast + The History of Rome), said once that one of his favorite books is Illuminatus! which is the series behind the game.

Someone might find this interesting.

Thank you again, and keep the thought provoking topics coming.

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I have feedback for the feedback section.

My comments regarding my government and government funded media was more about the irony of them having a fit about the labeling, despite the fact that they wrote and released the document stating that they were government funded. They could’ve just shrugged and carried on as normal, but they went into toddler tantrum mode over it.

Back in the '70s and '80s, they actually made some really good content with the money that the government gave them. These days, they get a lot more money, and most kindergarten classes could produce better content. While I do personally believe it is time for my government to stop funding them, it is more about economics than the fact that they are the propaganda department of the government here.

If the government really wanted to produce content, they could fund programs in schools and universities to both create the content and the business aspect of doing so, and while the content would likely be better, they would also be preparing people for future careers.

The truth of the matter is, I don’t feel the government here should be spending those billions of dollars on things that do not contribute in a positive or useful way to our society, especially given the current state of the economy. I realize that Canada is a first world country, but we have the following issues that we are not dealing with: Our government run healthcare system is in shambles and running on skeleton staff, while many workers have been banned from working in healthcare do to ongoing vaccine mandates (after 45 years of living in the same city, I no longer have a doctor). Many villages do not have clean or safe drinking water, which is a campaign promise to fix from Mr Trudeau at every election. Vast areas to not have high speed internet (I can physically see city limits from my house but have 1Mbps speeds on a good day). There is no mass transit once you leave large cities (I think amazing race just did a show on this). The electrical infrastructure is having more and more frequent outages, and electric cars haven’t even really taken off where I live yet. I could keep going, but the point is that we have a lot of current and upcoming problems that we are ignoring, while spending money in areas that there is no return on.

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I have to say, your assessment of the scientific process is incorrect. We are not going out to “prove” a hypothesis. See it more as an evidence gathering operation. We are supposed to be weighing information gained in an experiment, in the context of a hypothesis. Normally your null hypothesis is no difference between groups (usually control and treatment). When we get a significant difference, there is evidence to reject the null hypothesis, not that our proposed hypothesis is true. The null hypothesis still could be correct but in this instance, the way we have approached the test we have confidence to reject. When we continually test a system over and over, and we gather enough evidence against the null hypothesis, then we can conclude in fact that our proposed hypothesis is true, well as true as anything can be in sceince. I view the theory of evolution in this light. While still a theory, enough evidence has been presented that I can confidentially reject the null hypothesis (that evolution has not occurred or is not occurring). Now setting type 1 (alpha) and type 2 (beta) error levels is another big problem in sceince that needs to be discussed further.

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(Discourse complained that my reply should be at least 20 characters long, so here’s the filler)

Ha! Nice! These gyms are popping up everywhere. Especially if you have hipsters around, your area shouldn’t be an exception. Let me know how it goes!

Welcome to the forum and thanks for your feedback! Can you elaborate on this error level point? I actually have no idea what you are talking about. :grin:

I fucking hate that. I can probably turn that off as well…

I can, if nobody minds.

Science is ultimately about prediction: predicting the outcome of an experiment, predicting the future, if you will. I predict the sun sets today, and raises again tomorrow, and I make that prediction based on the scientific data. :wink: You do science by observing some thing and figuring out whether this observation allows you to predict something you weren’t able to predict before (the something is usually the result of some experiment).

The very idea of a null hypothesis and the fact that this idea is central to the scientific method as we know and use it stem from the Occam’s razor: our default suggestion is that the thing we are observing is just a fluctuation, happened by chance, and is not an evidence of anything important (that is, it can’t be used to predict anything). That default suggestion is called the null hypothesis.

You then construct an experiment (usually, a complicated series of experiments) to mathematically show (this usually involves hard-ass statistical methods that you need a colleague to explain to you how to even use them, let alone how they actually work “under the hood”) that your null hypothesis being correct is significantly less likely than your null hypothesis being false. Then you publish a paper making all sorts of (not entirely unfounded) claims based on that fact.

Of course, your conclusion may be wrong for a number of reasons, including the fact that “significantly less likely” in the previous paragraph is actually (almost) never “exactly zero chance” due to how stats and math work. And your error, if indeed wrong you are, can be one of two kinds:

  • type 1 error (aka 𝛼-error, aka false positive) is where you end up concluding that your null hypothesis was wrong while it actually is true; in fact, the thing you observed was a meaningless fluctuation, but now you’re trying to make predictions based on it; you’ll fail in the long run;
  • type 2 error (aka β-error, aka false negative) is where you end up concluding that you null hypothesis was true, while it actually is false; in fact, the thing you observed was important, but you failed to mathematically prove it, and you will not make the (valid and useful) predictions you could have made.

The two types of error lead to different outcomes, and may have very different costs in the particular field you’re exploring; consider treating patients with a non-working (but harmless) placebo vs. erroneously discarding a potent cancer treament that several billions of dollars have already been invested in; then consider a possibility of not noticing a potentially lethal side effect in a new vitamin pill that no one would really suffer without. Understandably, when resource-limited (that is, always), a scientist tends to set up the experiments so that a chance of type 1 error is much less than a chance of type 2 error (or vice versa, depending on which type is less expensive in the outcome terms); that’s what setting (tolerable) error levels essentially is about.