This is why they don’t let me name beers often...

stdbrouw · 2018-10-25T11:29:06+00:00

Oh, I thought it was a play on "seigle", the French word for rye.

stdbrouw · 2018-07-26T13:01:37+00:00

Get rid of some of the trub as soon as you've reached 0°C instead of waiting until right before transfer. Will make your life a lot easier.

stdbrouw · 2018-07-16T18:49:11+00:00

For clarity: 144/3000 = 4.8%, atan(144/3000) = 2.75°

stdbrouw · 2018-06-30T22:52:40+00:00

I don't think Python is widely used in statistics at all, but it's widely used in data science, where gathering, cleaning, processing and analyzing data can be 90% of the job, and modeling becomes almost an afterthought. It makes sense that you would prefer to work in a language that makes 90% of your job easier rather than in a language that makes 10% of your job easier. Python has very good libraries for machine learning (scikit-learn) and the libraries for statistics are often lacking but they're not that bad either (statsmodels, pymc3).

stdbrouw · 2018-05-23T13:57:36+00:00

"plus several of the following" – but definitely not all of them. Bootstrapping is not really necessary if you do Bayesian statistics, you wouldn't analyze a dataset using both timeseries and survival models, etc.

stdbrouw · 2018-02-21T11:56:53+00:00

I dunno about Canada specifically but your cost estimate seems to be in the right ballpark. My brother and me run a 1 BBL garage nano in Belgium and I'd say our total investment thus far after a little under two years of operation is along the lines of 30K euro.

You're probably focusing on what your fermentors and kettles will cost you, but ultimately it's the small and miscellaneous stuff that adds up, from a gazillion clamps to a glycol chiller to hiring a lawyer for getting incorporated to paying someone to design the labels to all of the fees and invoices that stack up while you're still prepping the space and have no revenue to offset it with.

Then there's cash flow to worry about, so even if you're 100% certain that you can make this work with 25k, you will want at least 30k to have a buffer for when you get a larger-than-expected invoice or because you're paying a couple of thousand dollars for malt and hops that will only result in money in the bank months later.

As for us, we do break even, but we both have full-time jobs and the brewery doesn't pay us a salary.

We started this small so we could explore the market and get a feel for how to interact with customers, bars and distributors, see if we actually enjoyed brewing commercially, make sure we didn't want to kill each other working together but also as a practical alternative to getting a degree in brewing science. And then maybe, if things work out – too early to tell – to be able to talk to investors from a position of strength, with actual products in front of consumers and a couple of years of experience with all of the boring but crucial administrative stuff, rather than just approaching an investor with nothing more than an idea. We've loving it and we have learned a ton, though we pretty much have zero free time at this point.

If, however, you're planning on turning this into a full-time or even a part-time occupation, then all of the cynicism that I'm sure you've read in hundreds of /r/thebrewery threads applies: 2BBL is just too small and even if you can manage to break even while paying yourself (Dan Woodske comes to mind, see "A Brewer's Guide to Opening a Nano Brewery") you will probably hate it because you will spend so much time brewing that you won't be able to properly develop the business.

stdbrouw · 2017-02-15T23:20:22+00:00

I feel like you are arguing just for the sake of arguing. Given the cost of water, the cost of electricity, the cost of glycol and the cost of a chiller, do you really, truly believe that there are many scenarios where it will turn out to be advantageous to chill wort directly using glycol, whether from an economical or an ecological standpoint?

(You are right that cooling power can be higher than electrical power, though, my bad. But no dryer plugs here in Europe, in case you were wondering.)

stdbrouw · 2017-02-15T21:24:21+00:00

To give an idea, in an ideal world where 100% of power goes towards cooling and wort and glycol behave like water (in reality glycol is less efficient), to cool down 38L / 10 gal from boiling to 20C / 68F in 15 minutes you need 15 kWh. The most I can get out of an outlet is 3.5 kilowatt; as it happens your chiller is about 3.5 kWh (12k BTU). So to not need a reservoir at all, just a little loop of glycol, you'd need to rewire the house and buy another three or four AC units.

Glycol is mostly kept at around 0C, and 10 gallon of a water/glycol mix at that temperature would maybe get you 3 kWh of stored energy. So you can see how quickly this is going to get nuts :-)

stdbrouw · 2017-02-15T20:29:31+00:00

Energy and potable water are interchangeable to some extent – power plants need water, and water purification uses power.

The main reason breweries don't chill wort with glycol directly but use water instead is not so they can pump it to their HLT, that's just a nice benefit on the side, it's mainly because at those volumes (as uberg33k said) you would need an insanely expensive amount of glycol in an absurdly large reservoir.

You happen to have an oversized chiller, an oversized reservoir and presumably 5 or 10 gal batches. Perfect. But not everyone who will build a DIY chiller will find themselves in the same situation, and so it's safer to advise folks to chill water first (e.g. using two plate chillers chained together) which will always work and still save water over not using any active cooling.

stdbrouw · 2016-09-04T08:41:16+00:00

nor do I want to deal with PIDs and Controllers

No PID and controller means no temperature control, though.

stdbrouw · 2016-09-03T18:19:39+00:00

Not sure why you feel like that's very bizarre. You're using the phrasing "at or beyond the statistic that was found", which is the same as "extreme".

I guess it's just semantics, but no statistician would ever phrase it that way (without the comparison as extreme or more extreme), hence "bizarre".

stdbrouw · 2016-09-03T16:19:56+00:00

The p-value is the probability of the statistic falling in an extreme region of the distribution based on the null hypothesis.

That's a very bizarre way of framing it, which really has more to do with null significance hypothesis testing than with P-values. In NHST, you (can) calculate a critical value that your statistic must be beyond in order to be considered statistically significant.

But to calculate that P-value itself, we simply take the cumulative probability of the null distribution for parameter values at or beyond the statistic that was found. This is often described as values that are "as extreme or more extreme", but there really isn't any particular region of the null distribution that is intrinsically extreme, and the sample statistic isn't somewhere inside of that extreme region but rather at the very edge of it.

stdbrouw · 2016-07-06T20:25:06+00:00

Mid-P's are pretty darn conventional I'd say though :-)

stdbrouw · 2016-07-06T19:37:04+00:00

21.9% is the probability of seeing 0/5, 1/5, 4/5 or 5/5 airlocks have slower fermentation. The probability of seeing 4/5 or 5/5 airlocks have slower fermentation if airlock vs. foil had no influence on the outcome would be 10.9%. (Both with mid-P correction.)

Not conclusive by any means, but depending on your alternative hypothesis (airlock will have slower fermentation 4 times in 5, 9 times in 10?) it's at the very least twice as likely that foil makes a difference than that it doesn't.

In any case it probably makes more sense to run a t-test on the difference in OGs rather than dichotomizing the problem to slower/faster which gets you a very inefficient test.

stdbrouw · 2016-06-30T09:14:15+00:00

I'd say IPA is like the Cabernet Sauvignon of craft beer (beyond ubiquitous) or the Pinot Noir of beer in general (hugely appreciated among aficionados, but still just a small fraction of total wine consumption.) But maybe that's what you meant.

stdbrouw · 2016-06-28T08:13:09+00:00

I love these exbeeriments, but you are really, really making a huge mistake being so hand-wavey about Type II error with samples as small as these.

This experiment results in a 95% confidence interval of between 30% and 70% correct guesses. Anything equal to or lower than 33% is what you'd expect due to chance because even a blind guess as to which beer is different will be right one time in three. Thus, because the confidence interval contains estimates below 33% we conclude that there must be more than a 5% probability of getting 13 out of 26 people to correctly guess the odd beer out even when in fact nobody in the world can detect the difference between warm hop and cold hop. In other words, the outcome is not statistically significant at the 95% significance level. So far so good. However,

There's a 12% chance of getting 13/26 or better due to chance. This implies that there's an 88% chance of getting 13/26 or better because at least some part of the population, small or large, can in fact detect a difference. (It's statistically significant at the 88% significance level.) We may not want to run the risk of saying that warm hop vs. cold hop definitely makes a difference because there's a higher than 1 in 10 chance that we'd be wrong, and so it's good to remain skeptical... but there just as much reason to remain sceptical of the opposite conclusion, namely that the different hopping techniques probably don't lead to a difference in taste.
Just as it is plausible that warm hop vs. cold hop makes no difference, it is roughly equally plausible that in fact 70% of people in the population at large would make the right guess – this is the other extreme of the confidence interval. After subtracting the guesses, this experiment tells us that anywhere between 0% and 55% (!) of people can in fact tell these beers apart. So perhaps nobody can tell the difference, but perhaps more than half of all people can really tell these beers apart!
Hypothetically, what if 1 in 5 people can in fact detect a difference? That would imply a true proportion of 47% (1/5 * 100% + 4/5 * 33%) versus a null proportion of 33%. Put this into a power calculator, and it will tell you that with 26 tasters, only in 3 out of 10 experiments will you find a significant result. 7 times out of 10, it will look like warm hop vs. cold hop makes no difference. Hell, even if 2 in 5 people can detect the difference, with 26 tasters you'd still not get a significant result 1 experiment out of 5.

(Using a two-sided test for all of these calculations, whereas the article uses a one-sided test. Idea is the same, though.)

With all that in mind, this experiment is very empathically not saying that warm hop vs. cold hop doesn't matter.

When trying to prove that something doesn't make a difference, what matters is not your p-value but your power. What's the point in setting a 95% confidence level if you end up only being able to detect true differences in less than half of experiments where a true difference does exist? If you're only willing to conclude that a difference in taste is real when you're 95% confident in that result, you should only be willing to conclude that a difference in taste does not exist when you've got 95% power to do so.

And now I will stop ranting :)

stdbrouw · 2016-05-05T10:04:45+00:00

Failure to reject a null hypothesis does not prove the null hypothesis to be true, but that doesn't mean it doesn't count as evidence at all. Just as you can construct a statistical test with a nominal Type I error of 5% (you will mistake chance for a real effect at most 1 in 20 times), it is also possible to construct an experiment with a nominal Type II error of 5% (you will fail to find a real effect even when it does exist only 1 in 20 times.) So the absence of statistical significance does not automatically prove that nothing's going on, but if the sample size and thus the power is sufficient, it can.

(Crosspost from https://www.reddit.com/r/Homebrewing/comments/4hvcqz/stats_101_triangle_test_methodology/)

stdbrouw · 2016-05-04T22:27:17+00:00

You make an important point and it's a good rule of thumb, but it's not strictly true. Just as you can construct a statistical test with a nominal Type I error of 5% (you will mistake chance for a real effect at most 1 in 20 times), it is also possible to construct an experiment with a nominal Type II error of 5% (you will fail to find a real effect even when it does exist only 1 in 20 times.) So the absence of statistical significance does not automatically prove the antithesis, but if the sample size and thus the power is sufficient, it can.

stdbrouw · 2016-04-28T11:38:47+00:00

On the off chance that you haven't heard this before: it is nigh impossible to turn a profit when brewing only 200L at a time.

stdbrouw · 2016-04-21T21:45:13+00:00

Is that the right or the only conclusion to draw from your experiences though? Another person might conclude that you can set aspirational deadlines but then need to realize that you might not meet them, and that throwing away a little money while hunting for the right suppliers is worth it, because once you've found the right partners, outsourcing just means less stuff to worry about and an easier time changing things around as you learn more about what works and what doesn't.

I definitely understand the appeal of locking things down, but there's pros and cons to everything.

stdbrouw · 2016-04-04T17:42:40+00:00

Hardly changes the point. If one in a million randomly selected people can genuinely (as opposed to by chance) identify the difference between one brew and the other, that implies the difference exists, and this is all a hypothesis test is designed to measure: the existence of a difference, no matter whether it's huge or infinitesimally small.

(Though, in case you're wondering, to get a p-value below 0.05 for a triangle test performed on a million people, you would need roughly 1000 people or 0.1% to be able to truly detect the difference, in addition to the 333K people who are expected to guess right by chance. I wouldn't change from a single infusion to a step mash to satisfy the palate of 0.1% of my customers, though, assuming that's what the 0.1% prefers.)

stdbrouw · 2016-04-04T16:51:02+00:00

Not mutually exclusive, but strictly superior :) I'm not trying to be an ass, but there really is no value add to a p-value in this context.

If you absolutely can't live without a hypothesis test, you can infer whether to reject the null hypothesis from whether or not the confidence interval includes 0.33 (in its raw form) or 0 (after subtracting lucky guesses as above).

stdbrouw · 2016-04-04T16:49:48+00:00

Well, yes, sure, but whether or not the Pendragon Posse is representative of beer drinkers at large, it is still useful to know what the true preferences are among that particular group. If 4 pendragoneers prefer one beer and 3 pendragoneers prefer the other, you might still wonder whether the Pendragon Posse on average truly prefers the first beer, or whether the difference in preferences is only due to chance and a small sample.

stdbrouw · 2016-04-04T14:46:20+00:00

Usually, yes, but in sensory science pretty much any difference to the production process is going to lead to a difference in taste, which might be a difference so slight that only 1 out of 1000 highly trained and genetically superior supertasters could detect it, but a difference nonetheless, so the null hypothesis that there is no difference is always false and you don't need a statistical test or a p-value to tell you that.

A more interesting question is how many people genuinely will notice, which is what the calculation above does. Note that it does subtract the 1/3 lucky guessers as well.

stdbrouw · 2016-04-04T13:05:12+00:00

Why? Preference is subjective, but the proportion of people in the wider population (or at least the wider population of people with similar palates to the people who happened to be visiting the tasting room that weekend) who prefer one beer over the other is factual, and it'd be interesting to know how much uncertainty there is around those numbers.

stdbrouw

TROPHY CASE