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This is the second part of a presentation to prepare you for this SPED4010 course. And in this
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part, I am going to say a few general things about what statistics is.
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So in general we can say that statistics is a tool in or a set of procedures and the role of this
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tool is to help us manage and handle uncertainty and variability. So is the way that we have to deal
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with the fact that things are uncertain and things and data are variable, people are variable.
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Treffsikkerhet: 91% (H?Y)
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And responses are variable and the numbers we obtain our variable and the they are uncertain and the
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performance of anyone and anything is uncertain. And we just need to be able to handle this idea
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that there is not perfect consistency. And where is this uncertainty? Well everywhere, so everything
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in reality in real life is uncertain and variable.
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The only place where there is no uncertainty or variability is in math in. Well, not every branch of
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math because statistics is also comes from a branch of mathematics, but you can find mathematical
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domains where things are certain and logic is another example, but everywhere else in every real
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part of life. Everything is uncertain and variable. The only tool we have to deal with it in
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way that leads to justified decisions is statistics. So statistics
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Treffsikkerhet: 90% (H?Y)
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gives us the possibility to evaluate all sorts of situations in which, of course, we are faced with
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uncertainty and variability, so that we can assess how much confidence can be put in proposals for
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doing things differently. In results that are reported in findings that are reported in guidelines,
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that are proposed methods decisions about our lives and so on.
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Statistics is indeed based on a mathematical branch of probability theory.
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As I said in the previous part of this presentation, we are not going to deal with this math. So the
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mathematicians and statisticians, develop the math that is behind the methods that were using in or
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just using them. What do we need is only to understand how to use them and how to interpret them. So
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we have to be focusing on the concepts.
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Treffsikkerhet: 79% (H?Y)
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Let us go back to the examples of part 1 to say just a few more words about what statistics does in
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helping us deal with each of these hypothetical situations.
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So, in the first example, there was talk of specific test scores, and there was the example of a
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score, a standard score of 69, in receptive vocabulary.
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Treffsikkerhet: 91% (H?Y)
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So what has standard score mean and what is 69? In order to be able to interpret this report you
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need to know about uncertainty in the score. So what if we tested this same child with the same test
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again, would you get the same score will almost certainly not. How far can we expect the second score
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to be from the first one? That is an inherent uncertainty in the test.
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Treffsikkerhet: 89% (H?Y)
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Then what if we tested with the different vocabulary score with a different vocabulary test? Would
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it get the same score? Almost certainly not. Then we need to know how much variability we have.
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that is a function of which test we chose to use.
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Treffsikkerhet: 91% (H?Y)
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And then, in order to understand what 69 means, we need to know something about, not the test so
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much, but the population. So if we were to give this test to every child, how many children would
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get more than sixty nine. How many would get less than sixty nine. This is the basis on which you
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can say whether this particular kit that we are dealing with has a low score or a very low score or
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an okay score.
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and what that means. So this is all about uncertainties and variabilities.
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Moving on to the second example.
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With this screening outcome. So, there was this screening procedure that is supposed to identify
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children with dyslexia. Well, first of all, we need to be able to know
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Treffsikkerhet: 88% (H?Y)
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something about the uncertainty of the outcome itself. So, if we were giving this screening test to
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children with dyslexia and the children without dyslexia, how often would the test get it, right?
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So, if we were only giving the test to children with dyslexia, would they all be identified as
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having as an in need of follow-up? If we're giving these tests of children, without dyslexia,
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would the test always say this
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Treffsikkerhet: 91% (H?Y)
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kids are okay. They don't need to follow up for dyslexia. So false, positives and false negatives
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are very important. That has to do with how well the test is performing, but that's not the whole
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story. Again. We need to know something about variability in the population. So what if we are
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screening for something that we expect about 5% of the population to be identified like dyslexia.
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Treffsikkerhet: 80% (H?Y)
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Or maybe we should be, we might want to identify maybe 10% if we want to be able to help children,
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who are kind of in the in the gray zone.
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Treffsikkerhet: 91% (H?Y)
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There are other kinds of tests where we need to identify people who are much rarer. So, in medical
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conditions, we may want to have screening test for something that occurs in every, in one person,
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every thousand or every 10,000. So very rare conditions. How do screening tests perform in such
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cases.
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Treffsikkerhet: 91% (H?Y)
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This is an issue about the variability of the population that combines with the uncertainty issues
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about the test itself and only one we understand both can we interpret results from this screening
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test and be able to decide whether we should use it or not, taking into account all the pros and cons
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of it. So given the special circumstances of our situation today. I thought to recommend you to
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listen to
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read and listen to some information about testing that are general. Actually, these are about
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medical testing. But whenever we have screening and diagnosis, whether it's medical as in the
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examples that I've linked to here or educational as in dyslexia, which is not a medical issue. In
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any sense at all, but are using similar concepts. We using responsive, like, screening. We are using
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the concept of diagnosis. So these kinds of
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concerns come up and you can check out these links sources. The videos, these videos are very short.
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There are few minutes each, so you can see the kinds of concerns that I'm talking about and how they
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must be understood in order to be able to interpret findings of screening tests.
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Treffsikkerhet: 81% (H?Y)
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I our third example, we had an announced Intervention, which was said to be highly effective.
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Treffsikkerhet: 91% (H?Y)
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Now, this is a very contentious issue. And in order to decide whether this is worth adopting, you
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need to understand what a research study like the one in this hypothetical example, what such a
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research study shows and this is counterintuitive.
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Treffsikkerhet: 88% (H?Y)
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Because what you first need to understand is what happens when you're sampling a group of
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individuals from a population. So here remember this was an announced intervention for difficulties
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in math. So there was a study of kids who received this intervention and improve their math skills
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as measured by a test.
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Treffsikkerhet: 84% (H?Y)
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So the hypothetical researchers who did this, they pick 10 kids to run the intervention on.
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Treffsikkerhet: 84% (H?Y)
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And this only makes sense if we assume that, what they find with these 10 kids can be valid to
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generalize over the whole population of children.
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Treffsikkerhet: 77% (H?Y)
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Now, I didn't tell you in the example, if they chose 10 children with difficulty or with a little
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difficulty, with a lot of difficulty or just ten random children, but that's the kind of information
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about variability that I didn't give you and that is critical to interpreting the test, but there is
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also the more general effect. So if you
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Treffsikkerhet: 82% (H?Y)
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have a large population of hundreds of thousands of children, and you pull out 10 of them.
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Treffsikkerhet: 85% (H?Y)
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And then you pull out a different set of 10. Are you going to get the same answer from your two
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samples? Probably not how different in the answer is be. This is very important because that helps
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you evaluate how much trust you can put into the answer from any single sample. Every study has one
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sample, but the weight interpreted properly is to think what could I expect if they had another
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sample?
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Treffsikkerhet: 91% (H?Y)
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So understanding variability, among different individuals children, and therefore among different
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samples from the same population is critical in being able to guide us to interpret finding such as
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these of intervention. Can we assume that a different sample of children would also improve in their
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math skills? And then how much would each child improve?
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Treffsikkerhet: 76% (H?Y)
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To say that they improved on average by 15% could mean that some children improved by 30% and some
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children didn't improve or that some children really improved very much and some children even got
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worse. So you can't know by just having an average estimate of improvement in a single test. Those
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are all these issues of uncertainty in variability that go in, that you really need to
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Treffsikkerhet: 86% (H?Y)
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have in the assessment of whether this kind of intervention is worth adopting or not.
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Treffsikkerhet: 90% (H?Y)
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Moving on to the fourth example. That was the one where you had an idea about an intervention and
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you tried it on 20 kids.
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Treffsikkerhet: 77% (H?Y)
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And 13 of them proved and seven didn't.
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Treffsikkerhet: 91% (H?Y)
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So the question is was this a good idea?
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Treffsikkerhet: 91% (H?Y)
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Now again, this is a little counterintuitive but to answer this question. The first thing you would
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do from a statistical point of view is not to go and study your intervention. But is to try to
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understand whether this kind of difference should be thought of as unexpected.
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Treffsikkerhet: 86% (H?Y)
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So is it unexpected to get 13 out of 20 to improve? Is it unexpected by chance? What does it mean by
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chance? By chance, means if nothing is really going on.
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Treffsikkerhet: 91% (H?Y)
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So if I have 20 kids, I measure them on some skill that's of interest to me for my work and then I
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do nothing and then I measure them again on the same skill.
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Treffsikkerhet: 91% (H?Y)
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So there will be some random variability that will depend on the reliability of the test. So the kid
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will not get the same score on both assessment on both assessment time points. And therefore, some
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of them may get a higher score in second time. Some of them may get a lower score. This is
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completely normal and expected and you shouldn't be surprised by it.
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Treffsikkerhet: 86% (H?Y)
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So if you got 13 to have a higher score, the second time in seven have a lower score.
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Treffsikkerhet: 91% (H?Y)
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Would that be weird?
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Treffsikkerhet: 91% (H?Y)
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If nothing happened, so if that would be weird, if that would be unexpected. Then it means. Well, if
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intervention got me to that point, then maybe it's a good one.
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Treffsikkerhet: 83% (H?Y)
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But if getting this difference by complete chance, by just reading the test is not unexpected. Then
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well, you can't say very much about intervention. It doesn't sound so promising after all. So the
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critical idea here is
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Treffsikkerhet: 87% (H?Y)
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chance. Imagine you flip a coin, 20 times, and you get heads 13 out of these 20 times not in a row,
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but just any 13 of them. And, of course, the other seven are tails, is that impressive is that
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unexpected without completely normal and, you know, not remarkable in any way?
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Treffsikkerhet: 84% (H?Y)
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So if you don't find 13 heads in 20 flips impressive, why would you find 13 improvements in 20 kids
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impressive? That's the way statistics goes, kind of in around, in a counter intuitive way of
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thinking, to highlight the role of chance and why is there all of chance important? Because
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everything is uncertain and
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Treffsikkerhet: 73% (MEDIUM)
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everything is variable.
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Treffsikkerhet: 91% (H?Y)
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And that we know for certain that everything is uncertain. Okay, that sounded a little strange. It
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actually is true. So if we know that everything is uncertain, then we have to learn and tune our
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intuition to appreciate what counts as unexpected, surprising or remarkable. And that's where we see
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the value of things. Possibly having made a difference. Because if
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Treffsikkerhet: 78% (H?Y)
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something is unremarkable, then we have no reason to think that something made a difference. So if
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you got the coin to come up heads 13 out of 20 times, you wouldn't think someone is tricking you you
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wouldn't think this is a strange coin. A fair coin with a 50/50 chance of heads and tails will
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pretty normally come up 13 or so or 12 or 10 or 14 times out of 20 heads, and you wouldn't be
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worried. If you got 19 out of 20 head
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Treffsikkerhet: 79% (H?Y)
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then you might be worried like what's wrong with this coin. This is coin is strange. But 13. Well
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not so worrisome. Well, then maybe you shouldn't be so impressed by this fictional intervention in
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this example. So going back to the more general description of what statistics does based on some
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intuitions hopefully drawn out of these examples, what statistics does for you is to give you
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procedures
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Treffsikkerhet: 85% (H?Y)
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by which you can calculate results. Of course, it also gives you a way of thinking the,
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what kinds of things you should be calculating to get your answer. And although the procedures are
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straightforward. The thinking behind the procedures is not entirely intuitive and that's why some
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people find statistics hard. It's not that the math is hard. So people tend to be scared by numbers,
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but the numbers aren't scary at all in statistics.
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Treffsikkerhet: 90% (H?Y)
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Nothing is really scary. But the way of thinking is unfamiliar. Do you really need to change your
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way of thinking and go in this roundabout way to understand what should be calculated and why? Now
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statistics doesn't just give you results. It always produces two kinds of things. Two parts of an
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answer together. That's part of doing a statistical calculation. You get an estimate.
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Treffsikkerhet: 82% (H?Y)
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And you get a confidence that's associated with this estimate. So, for every answer you get out of
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Statistics, you also get the degree of certainty associated with it. How much can you trust the
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answer? And that's the real strength, an asset of Statistics. That's why use it. We don't use it to
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find the number that's our answer.
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Treffsikkerhet: 76% (H?Y)
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That's not really very informative because that doesn't tell us anything about the uncertainty
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associated with the answer. So if the answer is two, but could be anything between one and three,
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that's very different from a case in which the answer is 2, but could be anything between 0 and 20.
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So the first answer is much, more useful. We know it's about 2. The second case, if it's 2,
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but could have been zero or
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Treffsikkerhet: 85% (H?Y)
00:17:45.950 --> 00:17:51.600
Could have been 20. That's not really useful answer. Just, that's what statistics does for us. Gives
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us a number and the confidence associated with that number. And the confidence is actually given in
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the form of a range. And you're already familiar with the idea of a range because you see that, for
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example, in polls in elections, that they say, the so, so party is predicted to get so-and-so
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percentage of the vote. And they say, the margin is
NOTE
Treffsikkerhet: 90% (H?Y)
00:18:16.000 --> 00:18:24.900
- 1% of plus or minus 3%. So you're already familiar with the idea that a statistical estimate comes
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with a range. So when they say that a party will get 20% of the vote. And there's the footnote that
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this, there's a 2% range. You know, okay, it's 20%. But it could be 18, could be 22 and the
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prediction would be correct anywhere in that interval.
NOTE
Treffsikkerhet: 85% (H?Y)
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So, that would be different from an another poll that says, predict 20, with an error range of plus
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or minus 5, so they would predict between 15 and 25 verses predicting between 18 and 22. So the two
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poles are clearly of different value, which means statistics gives you a sense of a likely answer.
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Treffsikkerhet: 74% (MEDIUM)
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Plus a sense of how much you can trust that.
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Treffsikkerhet: 83% (H?Y)
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And if this is usually expressed as intervals, so a interval is a range of values that are
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likely. And this is also a something that you need to start thinking about this new way of
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approaching answers, were used to answers as being points.
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Treffsikkerhet: 84% (H?Y)
00:19:32.700 --> 00:19:39.300
Point is a specific number. So indeed because people are more used to points and points are easier
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to process the the polls tells us. Okay, the party's going to get twenty percent. So that's a point.
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That's a specific number, but that's not what this study actually showed. The study showed between
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18 and 22. They give you the 20 as a point and they also give you the confidence interval. Say,
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okay. They started really is a plus or minus 2.
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Treffsikkerhet: 88% (H?Y)
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And the statistical way of understanding. This result is to think about the interval, not about the
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point.
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Treffsikkerhet: 89% (H?Y)
00:20:06.350 --> 00:20:15.800
And even when you have this tool the statistical tool, of course, it's still up to you to be able to
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choose the right procedure. So unfortunately, statistics is very rich. This means there are many
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different ways to do very different things. And you have to know which is right for the kind of
00:20:27.900 --> 00:20:33.100
question that you have for the kind of data set up that you have. And you also need to be able to
00:20:33.100 --> 00:20:36.900
interpret the answer that you get out of Statistics. So it's like a toolbox.
NOTE
Treffsikkerhet: 90% (H?Y)
00:20:36.900 --> 00:20:44.000
If you don't know how to use a hammer and a screwdriver, then you may be hitting with a screw and
00:20:44.000 --> 00:20:48.700
that's not a problem with the hammer or the screwdriver. That's the problem of not knowing how to
00:20:48.700 --> 00:20:53.300
use the tools. So, there is a similar issue with Statistics and that's why it's important to
00:20:53.300 --> 00:20:58.400
understand the procedures, and when they apply, and what they mean.
NOTE
Treffsikkerhet: 75% (MEDIUM)
00:21:00.100 --> 00:21:09.850
This is related to the more general context of research methods, which is the whole topic of 4010
00:21:09.850 --> 00:21:16.600
and has to do with what counts as a scientific question. So, what kinds of questions can statistics
00:21:16.600 --> 00:21:23.200
help you answer? The first thing that we need to understand is that hard questions can only be
00:21:23.200 --> 00:21:29.400
answered if they are sufficiently well specified. So what is it mean "Sufficiently well specified"?
00:21:29.400 --> 00:21:30.950
We will see an example of that.
NOTE
Treffsikkerhet: 91% (H?Y)
00:21:30.950 --> 00:21:40.200
They shouldn't be vague. They most of the questions that we have are actually not well specified.
00:21:40.200 --> 00:21:47.450
Because the only question is that statistics can help you answer is hypotheses about relationships
00:21:47.450 --> 00:21:53.600
among variables. So that probably had a few unknown words in there. And we're going to spend some
00:21:53.600 --> 00:21:56.150
time in the course to explain those.
NOTE
Treffsikkerhet: 91% (H?Y)
00:21:56.150 --> 00:22:05.800
In particular, the study of relationships among variables. It's how every branch of science works.
00:22:05.800 --> 00:22:12.800
So that's the physical sciences, the life sciences and the social sciences. So everything we can say
00:22:12.800 --> 00:22:15.950
that we know or know something about
NOTE
Treffsikkerhet: 91% (H?Y)
00:22:15.950 --> 00:22:23.500
it's because questions were posed in terms of relationships, relationships among variables
00:22:23.500 --> 00:22:31.150
variables were measured data were analyzed and answers were provided regarding these relationships.
00:22:31.150 --> 00:22:37.550
And these relationships in statistics often take the form of prediction.
NOTE
Treffsikkerhet: 91% (H?Y)
00:22:37.550 --> 00:22:41.850
Predicting one measure from another.
NOTE
Treffsikkerhet: 91% (H?Y)
00:22:41.850 --> 00:22:48.400
This is a little counterintuitive because that's not how we normally think about predict.
NOTE
Treffsikkerhet: 84% (H?Y)
00:22:48.400 --> 00:22:55.500
So we normally think about predictors having to do with the future. Like it pulled predicts voting
00:22:55.500 --> 00:23:03.100
out comes the this only makes sense if the ball was made before the voting day. Otherwise, you just
00:23:03.100 --> 00:23:11.300
count the votes. So in that sense, that's it actual predictive use of Statistics where you ask some
00:23:11.300 --> 00:23:15.400
people, what they're going to vote. And then you predict what the actual election outcome is going
00:23:15.400 --> 00:23:18.050
to be. But in statistics the word prediction.
NOTE
Treffsikkerhet: 79% (H?Y)
00:23:18.050 --> 00:23:24.050
isn't always about the future. We can say we can predict
NOTE
Treffsikkerhet: 75% (MEDIUM)
00:23:24.050 --> 00:23:28.199
how will you will do at school
NOTE
Treffsikkerhet: 84% (H?Y)
00:23:28.199 --> 00:23:36.500
Based on knowing something about you in kindergarten. So that's again a prediction. Right? That's
00:23:36.500 --> 00:23:42.400
it. It, it usual, the usual sense of the sense of the word prediction. You measure something in
00:23:42.400 --> 00:23:49.000
kindergarten. Try to predict, which kid will do well in school, but you can also go and say,
00:23:49.000 --> 00:23:55.100
second or third grade. It's specific time point. And say, can I predict how well this kid is doing
00:23:55.100 --> 00:23:58.500
in math, by knowing their language, at the same time
NOTE
Treffsikkerhet: 91% (H?Y)
00:23:58.500 --> 00:24:05.200
point, not a previous standpoint, can I predict how well this kid is doing in reading by knowing their
00:24:05.200 --> 00:24:07.200
vocabulary for example.
NOTE
Treffsikkerhet: 88% (H?Y)
00:24:07.200 --> 00:24:17.700
So this is trying to see if you can predict, if you can guess correctly, the values in one
00:24:17.700 --> 00:24:24.650
variable, for example, reading performance by knowing the values on another variable say vocabulary.
00:24:24.650 --> 00:24:31.550
This is important because if the answer is yes, if vocabulary and reading performance are related.
NOTE
Treffsikkerhet: 82% (H?Y)
00:24:31.550 --> 00:24:38.000
Then that's useful information. That's the answer that you might be interested in. And then of
00:24:38.000 --> 00:24:42.900
course, how you interpreted, and how you act upon it is not a statistical issue, but that's a kind
00:24:42.900 --> 00:24:49.600
of prediction and that shows how, what we mean when we say relationships among variables. So even an
00:24:49.600 --> 00:24:52.950
intervention is a relationship among variables.
NOTE
Treffsikkerhet: 90% (H?Y)
00:24:52.950 --> 00:24:56.650
So what does this mean? Can I predict
NOTE
Treffsikkerhet: 91% (H?Y)
00:24:56.650 --> 00:25:05.300
how well a child will do in a math test. If I know whether this child has received an intervention
00:25:05.300 --> 00:25:11.100
or not. So one variable is intervention or not. The other variable is a math test this relates to
00:25:11.100 --> 00:25:12.900
our previous example.
NOTE
Treffsikkerhet: 81% (H?Y)
00:25:12.900 --> 00:25:19.900
If I can predict, if actually, I can guess better
NOTE
Treffsikkerhet: 89% (H?Y)
00:25:19.900 --> 00:25:22.350
a child's math score
NOTE
Treffsikkerhet: 86% (H?Y)
00:25:22.350 --> 00:25:29.800
when I know whether or not they have received intervention compared to, when I don't know whether or
00:25:29.800 --> 00:25:35.300
not, they have received an adventure, if the guest is improved by knowing about the intervention,
00:25:35.300 --> 00:25:39.750
this, this is evidence that intervention has been useful.
NOTE
Treffsikkerhet: 89% (H?Y)
00:25:39.750 --> 00:25:48.400
So, this is a way, a very strange way of using the word predict, and a strange way of using
00:25:48.400 --> 00:25:54.100
relationships among variables to answer the informal question. Does the intervention work? And
00:25:54.100 --> 00:25:59.450
that's how you go about it. And that's the sense in which the questions that statistics answer, are
00:25:59.450 --> 00:26:05.900
relationships among variables. And that's the sense in which all Sciences work with hypotheses,
00:26:05.900 --> 00:26:09.450
about relationships among variables. So basically,
NOTE
Treffsikkerhet: 91% (H?Y)
00:26:09.450 --> 00:26:15.400
Everything we know about the structure and function of the world. Like I said, physical, biological
00:26:15.400 --> 00:26:18.650
and social domains. That's how we get it.
NOTE
Treffsikkerhet: 86% (H?Y)
00:26:18.650 --> 00:26:24.800
And of course, because we're doing all this statistically, we don't only have knowledge about the
00:26:24.800 --> 00:26:31.900
world. We also have knowledge about the uncertainty of our knowledge about the world. So we know
00:26:31.900 --> 00:26:39.400
what we can be confident about and what we aren't so confident about yet and need to work more on.
NOTE
Treffsikkerhet: 85% (H?Y)
00:26:40.200 --> 00:26:48.000
Let me now, go into an example of formulating, a research question, just to make sure this point is
00:26:48.000 --> 00:26:54.200
a bit clearer. So let's say you have some kind of informal question that you care about "Does coffee
00:26:54.200 --> 00:26:59.000
help studying". This is not a special education question, but it might be question that you're
00:26:59.000 --> 00:27:04.900
interested in knowing the answer because it's a student. You might be interested in knowing whether
00:27:04.900 --> 00:27:10.400
there are reasons to change your behavior. Like, are they reasons to increase your coffee intake are
00:27:10.400 --> 00:27:10.900
the reasons to
NOTE
Treffsikkerhet: 91% (H?Y)
00:27:10.900 --> 00:27:19.000
decrease it with respect to your study outcome. This may look like a very interesting and relevant
00:27:19.000 --> 00:27:26.500
question but is unfortunately not an answerable question. And the reason it's not answerable is
00:27:26.500 --> 00:27:31.400
because it's not specified. So what is coffee mean?
NOTE
Treffsikkerhet: 73% (MEDIUM)
00:27:31.700 --> 00:27:41.300
Is this amount of caffeine? Is it frequency of drinking coffee? Is it espresso versus Americano?
00:27:41.300 --> 00:27:49.300
Is it the fact whether you drink any coffee at all or not? What is the meaning of coffee in this
00:27:49.300 --> 00:27:56.800
question? And this doesn't have one right answer. There are many possible answers that you can give,
00:27:56.800 --> 00:28:01.100
but this makes you think about what do I really mean by this question?
NOTE
Treffsikkerhet: 90% (H?Y)
00:28:01.100 --> 00:28:08.800
So you have to specify what you mean, and that may even be the easy part because studying is the hard
00:28:08.800 --> 00:28:12.000
part. So what do you mean does coffee help studying?
NOTE
Treffsikkerhet: 91% (H?Y)
00:28:12.600 --> 00:28:21.500
Does it help? Does it help you be reading a book longer without falling asleep? Does it help you
00:28:21.500 --> 00:28:23.450
get better grades?
NOTE
Treffsikkerhet: 91% (H?Y)
00:28:23.450 --> 00:28:29.199
That's a very different questions. So what do you mean?
NOTE
Treffsikkerhet: 91% (H?Y)
00:28:29.199 --> 00:28:36.300
If you want an answerable question, it has to be very specific. So you have to see exactly what
00:28:36.300 --> 00:28:43.000
studying means. It could be hours over a book. It could be number of books read. It could be
00:28:43.000 --> 00:28:50.300
classes that are you got a passing grade on. It could be your average grade in an exam. It could be
00:28:50.300 --> 00:28:55.350
your average grade over your entire study period. It could be a lot of things. They're not wrong,
00:28:55.350 --> 00:28:57.150
but you have to decide.
NOTE
Treffsikkerhet: 91% (H?Y)
00:28:57.150 --> 00:29:00.300
and there's more like,
NOTE
Treffsikkerhet: 91% (H?Y)
00:29:01.300 --> 00:29:06.400
What do you mean does coffee help studying? Do you mean that should you drink coffee during the
00:29:06.400 --> 00:29:10.950
semester? Do you mean, should you be, should you drink coffee during the exam period? Do you mean
00:29:10.950 --> 00:29:15.800
should you drink coffee right before you read something right after you read something right before
00:29:15.800 --> 00:29:22.500
you go to an exam like throughout your study period of two years. What is the meaning of the
00:29:22.500 --> 00:29:29.100
question in that respect? Well, there's lots lots and lots of details that need to be specified
00:29:29.100 --> 00:29:31.950
before this interesting informal
NOTE
Treffsikkerhet: 79% (H?Y)
00:29:31.950 --> 00:29:36.500
question, becomes an answerable scientific question.
NOTE
Treffsikkerhet: 81% (H?Y)
00:29:36.500 --> 00:29:43.600
And the first step is in defining variables. So variables are defined by specifying. Well, in name
00:29:43.600 --> 00:29:50.100
we can call it coffee, but the issue isn't the name is the nature in range of values. So, what
00:29:50.100 --> 00:30:00.300
coffee means number of cups per day number of cups in a semester grams of caffeine a day or what?
NOTE
Treffsikkerhet: 87% (H?Y)
00:30:00.300 --> 00:30:06.900
And so the method by which they are assigned, do you actually ask someone? Do you measure? Do you
00:30:06.900 --> 00:30:13.600
observe? Do you give out coffee cups to people who are in this specific setting? So there are
00:30:13.600 --> 00:30:16.350
different methods to assign these values.
NOTE
Treffsikkerhet: 91% (H?Y)
00:30:16.350 --> 00:30:23.400
And the important thing here to repeat is that you can only answer specific well-defined questions.
00:30:23.400 --> 00:30:29.600
And once you have that, then statistic can become helpful. So if we were to map out all the route
00:30:29.600 --> 00:30:36.400
from a question to an answer, we generally start with a potentially interesting but vaguely
00:30:36.400 --> 00:30:42.900
formulated informal question, and that's true of everyone is not just true of people who aren't
00:30:42.900 --> 00:30:45.850
researchers, researchers also have this
NOTE
Treffsikkerhet: 73% (MEDIUM)
00:30:45.850 --> 00:30:52.000
starting point. So there is an interesting question, that's too vague to be answerable and then we
00:30:52.000 --> 00:30:56.850
make this question precise. And this means
NOTE
Treffsikkerhet: 88% (H?Y)
00:30:56.850 --> 00:31:04.400
define the relationships among specific variables and there are a lot of issues going in there. And
00:31:04.400 --> 00:31:09.400
you in this course, you're going to learn about operationalization and construct validity and issues
00:31:09.400 --> 00:31:16.400
that go in the route from what you have in mind to what you can actually measure.
NOTE
Treffsikkerhet: 91% (H?Y)
00:31:16.700 --> 00:31:24.500
And then you measure those and you get your quantitative data, and then these data are statistically
00:31:24.500 --> 00:31:30.400
analyzed and you get an answer about the relationships among the variables and you get the
00:31:30.400 --> 00:31:37.100
confidence that's associated with the answer. And then that answer is to be interpreted and acted
00:31:37.100 --> 00:31:43.500
upon. And the thing that I want to highlight here is that
NOTE
Treffsikkerhet: 89% (H?Y)
00:31:43.500 --> 00:31:49.050
This is where statistics comes in. So statistics is a link.
NOTE
Treffsikkerhet: 86% (H?Y)
00:31:49.050 --> 00:31:52.949
In a chain of research methods steps.
NOTE
Treffsikkerhet: 91% (H?Y)
00:31:52.949 --> 00:32:01.900
That concerns the analysis of quantitative data and there is a very similar chain of steps. If you
00:32:01.900 --> 00:32:07.000
were working with the qualitative method, except you would be gathering different kinds of data and
00:32:07.000 --> 00:32:12.600
you will be applying different kinds of procedures. And so you'd have different procedures for
00:32:12.600 --> 00:32:20.300
working with those kinds of data. The rest is pretty much the same. So that's why statistics is in
00:32:20.300 --> 00:32:23.250
the broader context of a research methods course.
NOTE
Treffsikkerhet: 86% (H?Y)
00:32:23.250 --> 00:32:31.800
And forms a one-step one link in the chain of things that you have to do to go from an interesting
00:32:31.800 --> 00:32:34.600
question to a usable answer.