 Okay, welcome to today's session. We're going to talk about statistics. We're going to learn what is statistics, what are the branches of statistics and what is the population and example, and what are the parameters and statistics. Let's begin. What is statistics? Statistics is a method of transforming the data you collected into useful information after you have manipulated it by analyzing or putting it into tables and charts for decision makers to make decisions. So we collect data from many different data sources. It can be from a database, it can be from a customer relation system, it can be from Facebook, what's up, anyway. So data is everywhere. So we can collect that information and we can summarize it by putting it into tables and charts and then also enriching it by making calculations on it by calculating the mean, the median, the mode and all the sort of thing and then present it to people who make decisions so that they can make informed decisions. For example, today's example, we're going to be using universities. So in a university or institution, we rely on data to make decisions. We rely on data to know when to increase the fees for students, when to build new accommodation for students, when to build new classrooms, when to buy equipment, how many people you need to accommodate, how many people are you expecting the following year. So those kind of information helps with decision making because if you know that data and you are able to manipulate it and calculate and present it so that people who make decisions are able to see where the gaps are and identify those gaps or the trends, then they can make decisions to solve and try and solve it imagine. If we want to mitigate against the risk of not having enough funds, not having enough seeds for students who are coming in, so we can use data to make decisions about certain information. Okay, so there are two types of statistics or the branches of statistics that you can do and that is the first one is your descriptive statistics. Descriptive statistics in its own, it tells you what it is. It describes the data that you get. You can describe it in terms of summarizing it in tables and charts and we're going to learn about it in chapter two and three about descriptive statistics. Then the other branch is your inferential statistics, where we draw conclusions about the population of study that you are studying from using the sample and we're going to learn about what is a population and a sample at the later stage and that you're going to learn in chapter four and chapter 11. That's part of what inferential statistics is more about. So descriptive statistics, like I said, we collect data and we can collect it from many different sources of data. It can come from surveys, it can come from transactional systems in your stores or at your case shares. We can collect information. When we do your registration, we can collect information from the system that you do registration on, whether it's a web application or not. We can collect information using the surveys or feedback forms and information can come from many different places. Then once we have that information, we can summarize that information in terms of tables and charts so that then it can be visual for people to understand. Sometimes it's easy when people see it visually and these days we use infographics for visualizing and telling the story because people tend to understand more when it's visual. Then the last part, we can also do descriptive statistics by summarizing it in terms of making calculations by calculating the averages, which are your means. We can find what is the mode or the median of your data or how far apart they are from one another, which is what we're going to learn in chapter three about the analyzing of the data. Then the inferential statistics, we can do it in two ways. We can use the estimation, which estimates your population mean by relying on what your sample mean was. We're going to learn more about what the population is and your sample is just in a few minutes. Or we can use a hypothesis testing when we test the clay. So as a researcher, you always need to make assumptions and you need to test those assumptions. We cannot be thumb-sucking things or information. We need to test them so that then when we make those assumptions about the population, we know that we are sure. We know that we are 95% sure that that thing will happen or 95% sure that that thing will not happen. So we need to be able to draw conclusions about the population using the sample. And that's what we're going to learn from when we do the confidence interval and the estimation and so forth. So the key concepts that we need to learn now, which from base of what the whole module is about, is the population and the sample. A population is a study of all elements that you're interested in researching. So since our example is high education, so let's say my population of study, I'm going to say I want to study all students who have registered at universities. And there are different types of universities. There are University of Technologies and traditional universities and different kind. But universities are all over the world. So they are all over. If I'm only interested in studying universities in South Africa, that is my population of study universities in South Africa. When I collect information from the people or the students that are in those universities, I'm going to manipulate those and calculate measures that describes the population. Like the median of your age or the color of how many students are from which race and so forth. Those we call the parameters because those measures describe the data we collect. Those are the measures that describes the data that we collected from the population. Sometimes the population is too huge. It's too big. We cannot do or we cannot collect information from the entire population. So we need to create a sample, which is a subset of that population. And it needs to be representative if we're going to be using statistics. But sometimes it doesn't have because then different types. There are methods of collecting the sample. Sometimes it can be a probability method. And sometimes it can just be a normal sampling method. And in this component, you don't have to learn about how do we do those, what is the process or that is followed to collect that information. All you need to know is we collect a sample and how that sample was collected at this point. It's not discussed in your module. Okay, so what is a sample? A sample, the subset of your population. It's just a small portion of the population that is representing the people from your population. And also with the sample, if we collect information from there, the measures that we collect from there are called statistics. And those are the measures that describe your sample. Okay, thank you for listening and that completes today's session. What I want to recap, we learned what is statistics, the method of transforming data into information for decision making. We also learned that there are two branches of statistics, which is your descriptive statistics, which just describes your data in terms of tables and charts and summarizing it. And then we also have what called inferential statistics, which means we draw a conclusion about your population based on your sample. And we describe what the population is. We said the population is a set of all elements of such. And from the population, when we collect data, there are measures that describes those data that we collected. And those measures, we call them parameters. And you will learn more about them in the later stage. Then we also learned that we cannot study the whole population, we need to create a sample. And a sample is just the subset that represents the population. And from the sample, the measures that describe that sample, we said we call them statistics. And we will learn more about them at the later stage. What do we mean by parameters? What do we mean by statistics? Thank you for listening. Let's chat on WhatsApp. I hope the concepts and the explanation I gave you today make it easier for you to understand the module much better. We will talk the next session that comes. We will do the types of variables. Thank you for listening.