 Riihon ja valit clownut ovat tietysti tärkeää kultaamista researchun. Ja niitä myös avataan yksi palo. Riihon ei ole kyse, että your study is reliable if repeated in the same study. Again, it gives you the same result. In quantitative research, the analysis is done by a computer, and ideally it is so well documented that if you do the study, again you do the exact same calculations. Koska komputeria ei ole mitään erilaisuja tai randomaisuja, niin se on vain se, kenen your study on ympäristöliä, koska asioita ei ole ympäristöliä. Se on se, että ympäristöliä on tietysti ympäristöliä, se on tietysti ympäristöliä, joka on assoitettu asioita asioita ja ei ole mitään erilaisuja. Validity on samanlaista asioita ja se voi olla ajattelua varmasti validitystä. Täällä on validity, joka on tosiaan, kuka asioita on tietysti valida, ja sitten on statistiastikkakonklus ja validity, jossa on tietysti, että asioita on kohdytta tai kohdullisista, jossa asioita on kohdullista. Siksi meillä on YMS, joka tarkastaa, että teokäät ymmärrän hoitakkoja... ... vai assois-an ja viimeistä kosesilta. Se on semmoisia, että assois-an jälkeen assoi on tehnyt korkeasti. YRS on sen, ominkin teokäyttä on verctavaa, joista kaikenlaista ei halua. Se on CNS-yhteyden, ja tässä ei ole tässä�pikasta. Se on seuraavesi. Two important qualities of measurement are reliability and measurement validity. To understand what those two concepts actually mean, it's useful to take a look at this target practice diagram. So this is a target that somebody is shooting. And here we have only a small amount of this person in the hits, but the sights are off. So this shooter is very precise, but he's not hitting the target. This is reliable, but not valid measurement. Then this is another shooter, which is not very very precise, so the hits are all over, but the sights are correct, so on average he is hitting the target. So this is not reliable, but valid. And there is some disagreement in the literature whether you can have validity without reliability. Let's postpone that for a while, but it's at this point important to understand what these concepts are. So the validity is whether the sights are correct and then reliability is whether the shooter gets the same spot all the time. So are you hitting the same spot, are you hitting the bull's eye? Which one of these is more serious problem? You can think of it as would it be safer to stand here in front of this target? Or would it be safer to stand here in front of this target? If my head was here in the bull's eye, this guy would eventually kill me. If my head was here, then I would be pretty safe, because this guy would never hit me. So the lack of validity is more problematic than lack of reliability, because an invalid measure is always incorrect and unreliable measure can sometimes provide you the correct value if it's valid. The idea of no validity without reliability basically refers to if you are just looking at one of these hits. So these hits individually are not very valuable because they're so dispersed, they're so unreliable. So in that sense if you just look at one hit on the target, it's unlikely to be close to the bull's eye. So that's the argument for no validity without reliability. But if you look at these as a collection, let's say these are five repeated studies and then after the study has been done, those five studies have been done, then we are trying to aggregate those somehow. Then as a collection, these five hits are valid because they are on average on the bull's eye. So reliability is a problem if you just do an individual measurement on individual study. Reliability can be less of a problem if you get to do multiple measurements and multiple unreliable measurements actually could produce you a valid inference.