So when you think of inference, it is interesting. There are really several steps that are important to keep in mind. There are two types of inference in the survey setting, one is the inference from a respondent to the characteristic of a respondent. And one is the inference from the characteristic of the sample to the characteristic of the population. What do we mean by that? Let's focus on the first part. The respondent to the characteristic of the respondent. Let's say you have an answer, very simple one. For example, the answer to the question, how old are you? You measure age, and you're interested in a larger concept, which is aging process of the cells, of the mind, experience, life experience in some form or what not. So, you get the respondent's answer, but the characteristic, aging characteristic that you're interested in, is already an inference leap from that particular item that you're interested in. And the answer to that item to the general concept that you're interested in. So, that's one piece of inference that we are doing here, all right? You infer from my answer to the question each. Something about my experience. My living conditions, my health, and the like. Now, with all the answers to the individual responses and the inferred characteristic with it, we can do some statistical computing, all right? So, we form an average of all the respondents. Answers and the implicit characteristic. If you come from psychology, you might be used to having multiple measures that form together a dimension, a psychology trait, a characteristic on a measurement scale that of course would also be in this first part. But then again, averaging that characteristic over all of the respondent would be one form of statistical computing that gets you at the characteristic of your sample, so you have values for everybody. And average age of a classroom and average age of a population. Careful of the population, only if you ask everybody. If it is a sample, then at first you have the average age of the particular sample. So, I take from all students of a university, a sample of students, let's say its random. Thousand students from a university that has 35,000 and I compute the age. That would be the step under here. Then there is an inference piece from that characteristic of the sample to the characteristic of the population. So if my sample average for the university students who are, let's say, 21, then I can do an inference to the entire population depending on how I drew my sample. I might directly infer that that same value is the characteristic of the population. But in order to do that, you have to design your survey in a certain way that these two inferential pieces do make sense. And that's what we're gonna talk about when we look at the different design decisions for a survey. But keep in mind, the two parts where inference happens. We will introduce one as sort of an aspect of measurement. A characteristic of measurement, an inference about measurement. That's the first part. And the other one, that's a characteristic of representation. Do the people that you gather information from represent and allow inference to the entire population?