Blog of Sara Jakša

Goals and Measurements

There is an interesting phenomena, when it comes to the goal setting and measurement. In economics, it is called the Goodhard's Law, but it also exists in the artificial intelligence and I have heard anecdotally, that it also exists in the hiring. Basically, what it means is, that when some statistical regularity is used as a target, then this target can break this regularity. It means, that just because something worked in the past, if we take it as a goal, it stops being a good predictor.

Lets take an very cliche and cartoony example from the personal health. But I think it is really understandable for explaining the principle. I don't know a lot about health studies, but I think there is a connection between health and not being overweight. Otherwise, they would not talk about obesity epidemic and be all panicky about children being more obese, because it is bad for their health? I mean, it is an assumption, but if it is wrong, then a lot of discourse in media is just... misleading :). So, lets now say, that because we want to be more healthy, the weight will be the crude measurement of that (I am from social sciences, so I am very ok with crude measurements). So we start dieting, and at one point, the people send us to the hospital, because we can't function normally anymore - which can be the effect of severe anorexia.

I guess I could say, that the very act of measurement can change the phenomena itself. I could bring quantum physics here, but I think that then I would only prove, that I don't know much about it. I can see this with myself. The very act of me putting every scientific article that I read in my bib file makes me more likely to try and finish the article, even if it is not that interesting. And that is without me checking how many of them do I read very frequently. I think the first time that I checked was for the New Year, and that was after more than a year of starting collecting. And I have not checked since. The books are the same, but there the effect is a bit different. Now, I am slightly more favoring the short books, than the long ones.

Well, reading a lot of books, short or long, and finishing more scientific articles, whenever they are relevant or not, is not a bad. But I am not checking constantly. Imagine how things change for people, that keep track of more aspects of their life. Quantified self movement comes to mind. How are these very subtle effect coming together to shape their lives?

Not to mention, that this is sort of done to us on a regular basis. Lanier in the book Ten arguments for Deleting Your Social Media Accounts Right Now calls them BUMMER (Behaviours of Users Modified, and Made into an Empire for Rent), which are the social media and search engines and personalized advertising and so on (the book itself is quite good, I can recommend it). How their algorithms collect the information about us, and then they use this as a target to predict our behaviour. Can you see the problem, in relation to the problem defined above?

What does this means for us? It means, that just because something can be measured, that does not necessary means, that it is a good goal. That we need to be more careful about how do we measure the goals for ourselves. But it also gives us hope. One thing that economics figured out, that when the governments were using inflation as a target, the Goodhart's Law kicked in and it became useless for prediction. But once it stopped being the measurement, it again become the good predictor. So, on the short term, I don't think it is that bad to use measurements like that. Lets say that one wants to be healthy. First one could try losing weight (if overweight), then start exercise more frequently and so on. Each measurement would only be under this effect for a short amount of time.

I guess even better would be to use multiple ones, since it is hard to go too much in one direction with multiple of them. It there really needs to be a measurement, then they could be combined, but summing them (better with regards to avoid overlearning) or as some sort of principle-component index (because not everything is a good indicator).

The best, but probably not so easy way, would be to use all the evidence and have a more holistic way of looking at the situation. Not everything can be measured and maybe our long-term improvement in live needs a bit more quantitative approach, not just qualitative.

Family Recipes

It is Lent right now, and even though I am not religious, I still decided to try and curb one of my addiction at this time. I my case, it was sweets, and I have to admit, that first days were harder, than I imagined. I guess there was some addiction there, like I would have joked for the last couple of years.

I think this is why I went through the recipes, that I had collected. I realized that way too many of them were sweets. Not counting sweets, there was only one recipe, that I still wanted to try. Another thing, that I could get rid of. (This is always a time for celebration :) )

I also found some of the recipes, that I got from my two grandmothers. Since I might regret not having them in the future, I will write them down here. Even if they are sweets.

What I always assumed where my grandmother cookies (but then I realized, that she did not put chocolate in them):


  • 200g of flour (polnozrnata pšenična moka)
  • 0,5 little spoon of baking soda (jedila soda)
  • 100g of butter
  • 75g of sugar (trsnega sladkorja)
  • 75g of chocolate
  • 1 egg
  • 1 little spoon of vanilla (vanilijev extrakt)

Maybe grandmother cookies (?):

  • 2 dl of water
  • 70 dag of butter
  • pinch of salt
  • 100g of flour
  • 3 eggs

Bake for 20 minutes at 200°C

Some chocolate cookie things that my other grandmother once got:

  • 3 eggs (whole)
  • 15 dag of sugar
  • 10 dag of butter
  • 32 dag of flour
  • a little of soda

  • walnuts, raisins, chocolate, (soncn? - maybe sunflower seeds? I can't read)

So I am putting it there, mostly so I make it easier to myself for throwing it away.

The Fear of Boredom

Do you know these conversations, where the conversation itself is a everyday thing, but they when you really think about it, you realize why there are problems in your life? Well, I recently had a conversation like that with a friend of mine. And there were three points in that conversation, that eventually led me to realize what is holding me back (and how useless it is):

  1. I told him that I am afraid of boredom
  2. He observed, that I know, how to shrug other ideas off
  3. I told him, that this is because I had too many of them

I mean, there are like everyday normal thing, that come up in the conversation all the time. Well, expect the boredom part, that one was a surprise even for me.

So things started to change very quickly after that. For one thing, the first thing I did was delete a lot of my files in the Later folder. This is a folder, where I keep all the things that I am currently working on: the articles that I want to read, the code that I am writing, the collection of ideas that I had and so on. A lot of it went away.

This is also an example of action coming before the insight. Because this is the first thing that I did, before I realized the rest of it.

Then I looked back, and I that insight came: I had a couple of interesting opportunities, that I did not follow up on, because I did not want to add something new to the plate. I did not know, how to handle another project on top of what I already had. So, I let the opportunities slip, which could make my life a lot more interesting.

But on the other hand, I am an INTP. I might not have the big vision stuff going on, but I can spot many interesting ideas in a single day. I mean, every time I come from the Python Meetup, I have more ideas that I could implement in a month, without having a conversation with a single purpose. I get way to many ideas from books or lectures and so on. That is before people offer me a chance to work on something.

And then I realized, that out of fear of not having enough opportunities (something my mind and the world already showed me that I do), I am sabotaging myself in taking them.

That made me realize, that I better finish the things that I started (my two master thesis, the working version of UExperience - though I know this will me a lot more long term as far as support goes, but by then, it will be a lot smaller demand on my time), eliminate what I don't want to do (I am looking at you Introduction to Cognitive Science 2) or things that I had procrastinated way too long on (like that analysis project of Arrowverse, which had from deep learning to graph analysis in it, learning lips and so on).

And I could eliminate a lot of internet communication. I am sorry, but I already have a hobbies that I can do, when I don't have energy for something else, and this is drawing and watching my favorite series in other languages (currently German). It is still going to be there, in case I ever need some more input, right?

I finally got to the end of this insight (about a week and a half after the conversation - it felt longer), when I was rereading the book Focus by Leo Babauta. Which talks about how to find some focus in our lives. And then the association when to the book Deep Work by Cal Newport and then to the So Good they can't Ignore You also by Cal Newport. And these three books were sort of a social proof, that I am on the right track. :)

I think this has been a lot easier for me, because I had dabbled in minimalism for years. So eliminating clutter is something that had become easier through time. Maybe for somebody else, the road would be a bit harder. And it is also interesting to see, if this road to minimalism will lead to bigger abundance.

But I think that even more important is, that we all need some sort of a mirror to see, what we are doing. So that in fear of something, we don't end up doing something, that will make it more likely to come true. Or, as a proverb, that I remember from one of the fanfiction stories: "One often meets one’s fate on the road one takes to avoid it."

I need to learn to post blogs when I write them as well - this one was posted more than a week after writting it

Learning Statistics with Basketball

As I am reading some of the statistical articles, to help me with the master thesis, I had come across one, that might be interested to more people.

This short (4 pages) article talks about how high-school students could be more motivated in statistics, but comparing Michael Jordan and LeBron James. So it is interesting to people who had interest in basketball (there are some analysis and data there), education and statistics.

The article is Understanding summary statistics and graphical techniques to compare Michael Jordan versus LeBron James

Social Skills and Math Skills


I had to tutor a lot of people in mathematical or statistical part of economics and in programming. And a lot of times (though far from always), there were these moment, when I could see that people were not getting it, but I was not sure why.

I remeber a case in my master studies, where one of my classmates from abroad had problems, and wanted my help. So I said yes. Well, the problem was, that I eventually found out, that he did not even uderstand some of the basic mathematical principles. Like using the letters to stand for the numbers and linear regression. How does one explains this? I don't know, because to be, it was clear from the start.

It was only when I started tutoring in the programming, that I was able to formulate some hypotesis. One interesting things, that I had seen, with the people I was tutoring, was the use of the theory of mind. At least some of their problems steamed from their expectation, that the computer has a theory of mind and that this is a conversations.

I mean, it is, but it is a lot more structured. It is a conversation, where everything has to be explicitly said or was at one point explicitly agreed on, and these agreements can be checked. There is no reading the intention going one.

Maybe this is the reason, why some people are afraid of the code? Because the code/computer might take offense?

So, my hypotesis was, that this social understanding would impeed the ability to do math and code.

I found the article titled The Empathizing-Systemizing Theory, Social Abilities, and Mathematical Achievement in Children, that used the systemizing-empthatizing to try and research this, in out case, the empthazing would be connected to the empathy/social skills and so on. The people lower on empthaizing were better at math (calculating at this level). Systemizing was not connected in this stage.

But by the time people come to the university, systemizing was conneted to math intelligence, as discivered in article Systemisers are better at maths. Subject and gender differences in math dissapread when controled for systemizing.

This is as far as ability goes. In the article Testing the Empathizing–Systemizing theory of sex differences and the Extreme Male Brain theory of autism in half a million people, one of the analysis that they did was the difference in systemizing and empathizing between STEM and non-STEM employees. The STEM employes were lower in empthaizing (beta = -1.10) and higher in systemizing (beta = +1.27).

Now, if anybody is interested in the short, but very readable analysis of gender differences in STEM, I suggest the report Why don’t more girls choose to pursue a science career?

So, as far as the literature goes, there is some indication, that mathematical interest or skill could be connected to social skills.

So in order to get one more piece of the information, I will try to find some data and do analysis in this direction as well.

Country Level Analysis of Agreeablness and PISA Mathematical Scores

While the effect on the country levels can be different than the effects on the individual levels, it can still be useful to check it. Expecially, since a lot more country-level data is already avalable.

First I combined two dataset. The personality came for the article The Geographic Distribution of the Big Five Personality Traits: Patterns and Profiles of Human Self-Description Across 56 Nations, which can be found here: . And the mathematical data came for the 2015 PISA results, that can be found here: . I combined the dataset, so it only included the countries, that were represented in both (40 countries).

data_country = read.csv("data/data-personality-math.csv")
Country Math E A C N O
Argentina 409 49.10 42.75 48.18 55.05 50.83
Australia 494 48.98 47.51 45.87 50.82 50.07
Austria 497 50.61 45.90 46.73 49.69 49.29
Belgium 507 45.99 45.07 43.03 53.60 54.59
Brazil 377 45.89 45.86 45.38 53.14 49.16
Canada 516 48.32 49.14 49.05 50.58 48.75

Now, the social skills (that I am interested in) is most connected to agreeablness. So this is what I am going to be using for this analysis.

Let us first see the distribution of the two variables, that will be used.

ggplot(data_country, aes(x=Math)) + 
       geom_histogram(bins=20) +
       xlab("PISA Avreage Score Levels") + 
       ggtitle("Distribution of Mathematical Skills in Dataset")

Histogram of Mathematical Skills

ggplot(data_country, aes(x=A)) + 
       geom_histogram(bins=20) +
       xlab("Avreage Agreeablness Level") + 
       ggtitle("Distribution of Agreeablness in Dataset")

Histogram of Agreeablness Levels

And now for the connection.

ggplot(data_country, aes(x=Math, y=A)) + 
       geom_jitter(width=0.01, alpha=0.3) +
       ylab("Avreage Agreeablness Level") + 
       xlab("PISA Mathematical Level") + 
       ggtitle("PISA Math Skills and Agreeablness")

Scatterplot of Math Skill and Agreeablness Level

PISA results are standardized to 500 mean and 100 standard deviation over all the countries. The personality took US as standard and had all the countries standardized with US having the mean of 50 and standard deviation of 10. Since the scaling can be potential problem in statistical analysis, I will devide PISA scores with 10. This way, that will both be standardized on the similar scale (but not the same).

data_country$Math <- data_country$Math/10
Country Math E A C N O
Argentina 40.9 49.10 42.75 48.18 55.05 50.83
Australia 49.4 48.98 47.51 45.87 50.82 50.07
Austria 49.7 50.61 45.90 46.73 49.69 49.29
Belgium 50.7 45.99 45.07 43.03 53.60 54.59
Brazil 37.7 45.89 45.86 45.38 53.14 49.16
Canada 51.6 48.32 49.14 49.05 50.58 48.75

Now, I will build a linear regression model, where I will try to predict mathematical scores from agreeablness.

model_country = lm(data_country$Math ~ data_country$A)

While the coeficient is -0.24 (so in the direction, that I would predict), I did not have a high enough sample to make it statistically significant. So this might just be a chance of random fluctuation.

lm(formula = data_country$Math ~ data_country$A)

   Min     1Q Median     3Q    Max 
-9.595 -2.489  1.317  3.140  6.736

               Estimate Std. Error t value Pr(&gt;|t|)    
(Intercept)     58.4208    12.7601   4.578 4.91e-05 ***
data_country$A  -0.2426     0.2708  -0.896    0.376    
Signif. codes:  0 *** 0.001 ** 0.01 * 0.05 . 0.1   1

Residual standard error: 4.596 on 38 degrees of freedom
Multiple R-squared:  0.02069,   Adjusted R-squared:  -0.005086 
F-statistic: 0.8026 on 1 and 38 DF,  p-value: 0.3759
ggplot(data_country, aes(x=Math, y=A)) + 
       geom_jitter(width=0.01, alpha=0.3) +
       geom_smooth(method=lm, color="red") + 
       ylab("Agreeablness") + 
       xlab("Math Skills") + 
       ggtitle("Connection Between Agreeablness and Math")

Graph of Connection Between Agreeablness and Math Skills

Just for my own interest, I want to see, which one would have the highest effect amoung personalty dimentions. I will again use linear regression.

model_country_5 = lm(data_country$Math ~ data_country$A + data_country$C + data_country$O + data_country$E + data_country$N)
lm(formula = data_country$Math ~ data_country$A + data_country$C + 
    data_country$O + data_country$E + data_country$N)

    Min      1Q  Median      3Q     Max 
-10.053  -2.184   1.636   2.686   5.458

               Estimate Std. Error t value Pr(&gt;|t|)   
(Intercept)    151.4562    54.6188   2.773  0.00895 **
data_country$A  -0.0740     0.3350  -0.221  0.82649   
data_country$C  -0.5721     0.3204  -1.786  0.08306 . 
data_country$O  -0.3000     0.2818  -1.065  0.29452   
data_country$E  -0.4551     0.6164  -0.738  0.46542   
data_country$N  -0.7286     0.4199  -1.735  0.09177 . 
Signif. codes:  0 *** 0.001 ** 0.01 * 0.05 . 0.1   1

Residual standard error: 4.42 on 34 degrees of freedom
Multiple R-squared:  0.1896,    Adjusted R-squared:  0.07048 
F-statistic: 1.591 on 5 and 34 DF,  p-value: 0.189

It seems, that when all the personality dimentions are added in, the agreeablness have the least affect. Instread it seems that neuroticism and maybe openess are much more interesting in this regard.

Disturbing Technology Design

Here I just wanted to share an article with you all. The article Motivating Learners by Nurturing Animal Companions: My-Pet and Our-Pet (actually a scientific conference presentation, but who cares) talks about, how the Tamagochi-es were used to make students more motivated for learning.

I mean, there are ethical problems already - the first of all the assumption that more studying (and cooperation) is good, which my itself is moral evaluation to the people having a worse emotional landscape because if it. Yes, I am sure of this, since they included the quote in the article, which was something like "Id the pet is sad, then so am I". Don't forget, these are children in school, not adults.

People anthropomorphise machine (just check, what happened to people talking to ELIZA), but we generally don't make people worse off, because somebody else did not do something. Except in action movies, where this is done by the bad guys. But for the people with very strong anthropomorphising, I don't think there is much of a difference. So I am left wondering, did nobody find this manipulative?

(Well, except my professor from Vienna, who mentioned this as a lack of ethical standard in human-computer interaction research until recently)

What to do to Save the Planet

I have recently read two of the scientific articles about the environment protection. The first one, Comparative analysis of environmental impacts of agricultural production systems, agricultural input efficiency, and food choice dealt with comparison of the food choices, with plants being the best, followed by animal produced vegetarian food, poultry and pork, fish and last beef, goat and lamb meat.

But the was the second article that was more interesting. The article researched how many times different environment protecting actions were recommended in high-school textbooks. But the interesting thing was the classification of actions into low-, middle- and high-impact actions. Beside one high-impact (which I found disturbing), I am listing the actions with their impact level below:

Action Impact
Live car free High
Avoid one flight High
Purchase green energy High
Reduce effects of driving High
Eat a plant-based diet High
Action Impact
Home heating/cooling efficiency Middle
Install solar panels/renewables Middle
Use public transportation, bike, walk Middle
Buy energy efficient products Middle
Conserve energy Middle
Reduce food waste Middle
Eat less meat Middle
Reduce consumption Middle
Reuse Middle
Recycle Middle
Eat local Middle
Action Impact
Conserve water Low
Eliminate unnecessary travel Low
Minimize waste Low
Plant a tree Low
Compost Low
Purchase carbon offsets Low
Reduce lawn mowing Low
Ecotourism Low
Keep backyard chickens Low
Buy Ecolabel products Low
Calculate your home’s footprint Low

Some Interesting Facts About Individual (and Team) Differences

Here I am going to write about some of the interesting individual differences, that I have read about in the recent weeks.

The first one if from the article Worth Less?: Why Men (and Women) Devalue Care-Oriented Careers. It seems that women prefer having a HEED (healthcare, early education and domestic) roles to STEM, while men prefer to have a STEM job over HEED job. But as far as valuing the job, both jobs value the STEM jobs about the same, while the women value HEED jobs more than men. The difference in value comes from the difference in communal values. Both would pay STEM more than HEED, but the difference in payment was bigger for men and women.

The second one is from the article Personality Predicts Obedience in a Milgram Paradigm. This article reproduced the Milgram experiment as a test run for a show (now sure, if the show was ever supposed to be run). After excluding the participants, that were aware of the Milgram experiment, the people that were more agreeable and more conscientious people were more likely to administer electric shocks to the learner. Left-leaning and female active in political activism were also less likely to administer electric shock. To me, left-leaning was expected, since right-leaning are usually higher in conscientiousness. But female political activism was not, because women are higher in agreeableness and I don't understand what effect would political activism have.

The third one is from the article Revisiting the Stanford Prison Experiment: Could Participant Self-Selection Have Led to the Cruelty?. They tried to see, if advertisement for participating in the prison experiment would bring in different people than advertisement for a psychological experiment with no mention of setting. The differences with the predictive validity were aggression, narcissism and social dominance. This could potentially explain, how could the Stanford prison experiment ended so badly (if you don't know it, Google it, since it is quite interesting).

The forth one is going to combine the three article, since they talk about similar things. The article The measurement of dominance in pregnant women by use of the simple adjective test showed, that aggressive and dominant women were more likely to bear male children than non-aggressive and non-dominant women. The article Dominance and testosterone in women then showed, that women that score higher in the dominance test, used in the previous article, have a higher level of testosterone in blood. Exposure to testosterone in womb can be measured by the ratio of second and forth digit. And in the last article in this section, The Impact of Prenatal Testosterone on Female Interest in Slash Fiction they used this to show, that women that were exposed to more testosterone in the womb were more likely to read slash fiction. I think I remember reading long ago, who testosterone is also connected to the interest in stuff over people, but I can not remember the article now.

The fifth one is going to be in subject differences. Mostly, how are economists different from other people. The first article is Does Studying Economics Inhibit Cooperation?. In review of some of the already done experiments, they note that economics professors (in comparison to other professors) are less likely to give money to charity, but they are equally likely to volunteer and vote. Then they had people go through the prisoner dilemma in different conditions. The economists were more likely to deflect, when there was no chance to make a promise with a person. They also checked, how likely would a person return strangers 100$ and report a billing error in their favor. They tested it before and after the class. The game-theory class had the most negative effect, the astronomy class the least and the socialist economics were on the middle. The article Business education and erosion of character speculate that this is because of the model used in economics and students making the leap from this is how we model people to this is how people ought to act.

Added 2019-03-11: I found notes from reading another article in differences between economists and psychology. In the article The influence of academic discipline on empathy and psychopathic personality traits in undergraduate students they showed, that student of psychology have a higher level of empathy (no matter if they measured cognitive, affective or general level of empathy) than business students. The article Who volunteers in psychology experiments? An empirical review of prosocial motivation in volunteering instead looked at the more behavioural level. They tried to figure out, who volunteers for participating in research. And the difference in motivation is what explained the difference (psychology students volunteered more than economics students). Among psychology students, there were 57% of people with prosocial motivation, 37% individualistic motivation and 6% of competitive motivation. Among economic students, the picture was different. There were more people with individualistic motivation (with 47%) and competitive motivation (17%), but less people with prosocial motivation (36%), when compared to the students of psychology.

The next one is not from the scientific article, but from the book chapter. It is Suppressing intelligence research: Hurting those we intend to help by Linda Gottfredson. There was a lot packed in that chapter. But I think the most interesting was, how much IQ can affect life and how much politics can stop the science, which could eventually help them, just because they don't like the results. There is a small percent of people, that can not do the simple task of locating the expiry date on their ID. I think the intelligence issue is one of the most important, but neglected issue today, and this is one of the simple ways to dive in.

The last individual one is from the learning perspective. The article Reviews Matter: How Distributed Mentoring Predicts Lexical Diversity on tries to quantify the effect of review on the writing skills. 650 reviews are about the equivalent to the year of improvement in adolescence, when controlling for both fandom and age of the writer. So even a distributive mentoring through comments can be helpful.

The last one is more team oriented. The article Large teams develop and small teams disrupt science and technology researches the scientific impact and the size of the team. The smaller teams were more disruptive, coming up with new ideas, while the larger teams were more developing, so building on an existing ideas. Teams that were funded for the project were also more likely to be developing teams. Nobel prize winners were more likely to be disruptive. And another interesting fact, the disruptive teams were more likely to cite older and/or less popular stuff.

Notes about Statistics in Social Sciences

There are just two notes, that I think are interesting and they are relevant to the social sciences.

The first one comes from the Many Analysts, One Data Set article. This article gave multiple teams the same data and hypotesis to research. There were still a lot of differences in aproaches the teams took, the variables that they used from the dataset and the effect that they come. So they then recomend, that single analysist would need to make as many analysis as possible, and calculate how many of them would need to show an effect, to be confident that there is an effect. Even better would be, if analysis in science would be done in a crowdsourcing matter.

The second one comes from the One Hundred Years of Social Psychology Quantitatively Described. This used all the possible researches, that they could find, to try and see what kind of effect is expcted for each variable. This can be useful when testing hypotesis when using Basiyan statistics or for (at least) some calculations fo power and needed number of participants for that power. The table of effects is below.

Effect Number of Meta Studies Effect Size Standard Deviation
Agression 31 0.24 0.20
Attitudes 32 0.27 0.14
Attribution 36 0.14 0.14
Expectancy effects 16 0.16 0.22
Gender roles 19 0.18 0.13
Group processes 27 0.32 0.15
Health psychology 22 0.17 0.13
Helping behavior 14 0.18 0.16
Intergroup relations 28 0.19 0.18
Law 25 0.17 0.08
Leadership 42 0.25 0.18
Methodology 29 0.21 0.10
Motivation 12 0.15 0.12
Nonverbal communication 29 0.22 0.17
Personality 32 0.21 0.14
Relationships 32 0.22 0.12
Social cognition 22 0.20 0.19
Social influence 26 0.13 0.18
Total 474 0.21 0.15

I am quite interested in the personal differences, so I also copied the who things, where they went a bit more in the peronality difference. The first is the personality vs. situation problem (disclamer: they are about the same):

Effect Number of Studies Effect Size
Personality 16,282 0.19
Situation 17,631 0.22

And then for the sex differences (sans cognitive differences, since these are only variables from social psychology):

Effect Number of Studies Effect Size
Sex differences 83 0.12
Sex targets 0.08
Sex actors 0.13

Collection of Links for Websites

I am quite proud, that I no longer have any bookmarks. But then I do need a place for all the links, that I might be needing some day. So blog posts became sort of dump for it.

Plus, if it is something, that I am not comfortable posting on this blog, then it is probablly not something that I should be doing anyway (and no, I still believe privacy is very important).

Here is one link, that can be used to check, where the site on certain domain is hosted:

And here are some sites, where one can check how big and quick the site is. And the loading times, because who will wait for the site to load:

Added 2019-03-07

Here are some of the sites, that allow you to see, with what technology is the site build with: