 Saksituminen tärkeää ympäristöä liikotus on kylmästysjärjestelmää, ja joten on kylmästysjärjestelmää yksittäisöstä. Kylmästysjärjestelmää on tärkeää, koska se on mahdollisuus, joka ei ole valitettava. Nyt tajuttamme, mitä leitösti liikotus on mahdollisuus, ja mitä mahdollisuus se on. Idealaisuus on, että jos ymmärtämme samaan ympäristöä, niin ymmärtämme samaa suunnitelmaa. Joten se on ymmärtämällä ympäristöä, joka ei ole randomnoissa. Tässä on esim. 3 ihmisiä, jotka ovat ymmärtäneet 3 ympäristöä samaa vaatimuksesta. Tämä vaatimuksesta on ymmärtävä ja ympäristöä, jossa on ymmärtää ympäristöä. Joten se on ymmärtävä ympäristöä, mutta ymmärtävä ympäristö on variaista ympäristöä ja se on randomnoissa ympäristöä ympäristöä. Joten tämä ympäristö, joka on vähän paljon 60 kg, on 3 ympäristöä 60, 60, 1, 59 kg. Ympäristöä on aika kai, joten variaista ympäristöä on 8,19 ja variaista ympäristöä on 8,39, joka on vähän enemmän kuin ympäristöä. Joten tämä on myös ympäristöä, joten nämä ovat 80, nämä ovat kaikki 70 ja nämä kaikki 60. Joten se on hyvin ympäristöä, joka on tärkeää päristöä. Joten se on ympäristöä. Ja kun meillä on tämä kai, jota haluaa kauttaa ympäristöä ja ympäristöä, jota voimme sanoa, että se on ympäristöä ympäristöä tai se on ympäristöä ympäristöä. Ympäristöä ympäristöä on tärkeää, että se ei ole mitään informaation tämän fenomenon tai variaationen fenomenon. Joten ympäristöä ympäristöä on tärkeää, että se on tärkeää ympäristöä tai tärkeää ympäristöä every time when somebody is, when the same person is being measured. That is different from the reading being correct, because reliability doesn't address any systematic measurement errors. The reliability concept comes from classical test theory and the classical test theory is basically this equation here. The idea is that the measured score x is a sum of true score plus some random noise and the only thing that is the measurement there is random noise so there's no systematic error here. So it's a very simple theory and it gives us the definition of reliability. Reliability is defined as the squared correlation between x and t or r square of regression of x on t or if we have standardized estimates 1 minus variance of e or the share of variance of measured scores x that can be attributed to the true score t. So basically it is the amount of how large share of our observed variance of the variance of x is due to the random noise and how much is due to the true score. So it's like a signal to noise ratio. So the reliability is simply defined that way. So the ratio of true score variance to total variance. So where is validity in this theory? Turns out that classical test theory is not really a measurement theory because it doesn't address validity. Instead it is a theory that allows us to define reliability. So classical test theory doesn't really tell us what the t is and we cannot particularly infer that the t we cannot make claims based on classical test theory that the t would be a score of any particular construct. The t is simply whatever would be the long run measurement result if we repeat the study over and over and over of the measurement over and over and over using the same measurement instrument and the same subject. This is very clear from the original works by Lord and Novik who formalized this theory. They explicitly say that the true score t in classical test theory does not necessarily agree with any construct score and it may not even be a valid or useful measure of any particular construct. So this theory basically gives you just a definition of reliability and that's it. There are some problems that people see with this theory. The first problem is that it assumes that errors are purely random noise. So there is no room for systematic measurement error here and oftentimes we could have measurement error, systematic error. For example we could have a bathroom scale that always shows 10% too much or 2 kilos too much. That is beyond the scope of this theory. Also it doesn't address validity at all. But this is a useful theory because it gives us reliability and it's a theory for reliability and you should not ask it more than what it provides. But there's still one more final question. If you only think about what is reliability, the question is that if the t here, the true score is simply the part of x that is reliable. What exactly is t? So that's the theory it doesn't answer and that's a validated question. So it's not a question about reliability. There is also one interesting feature about reliability. So let's take a look at the bathroom scale. So the idea of reliability was that the reliability is the true score variation here 8.19 divided by the actual observed score variation 8.39. So this is 98% reliable. What will happen if everybody is the same weight? So if our data are here, the variance of real weights is zero and bathroom scale reading varies between the variance is 0.67. It turns out that reliability is zero because it's zero divided by 0.67. So if there is no variation in the population or no variation in the sample depending on whether you are interested in the population reliability or the sample reliability, then the reliability will be zero because it's ratio of true score variance divided by the total variance of the data. One way to understand this is that any readings here, any variation of the readings here is purely due to measurement there because there's no variation. So this variation here doesn't tell us anything about how these people vary on their weights because there's no variance. One way to understand or another way to understand this issue is that reliability is an index of precision compared to the required precision. So here we have, this is slightly imprecise because we have for example this person there are weights, the measured weights vary between plus and minus one kilo and but that is a sufficient degree of precision to say that this person is the lightest person because there is so much variance in the real weights. Here to say that these people who are the same weight to the second decimal, the first decimal, then we would need a lot more precision to say which one of these people is the heaviest, that which one is the lightest. So therefore the reliability for this scale for making inferences on who of these is the lightest or who of these is heaviest person is very low and it's actually zero. So reliability is the ratio of the precision of the instrument and the actual variance of the thing that you are measuring.