 Hello, I welcome you all once again to my channel Explore Education, I am Dr. Rashmi Singh, Assistant Professor, Department of Education, SS Khanna Girls Duty College, University of Allahabad and this time I am going to discuss with you all a topic which I have been never touched before, only a few videos of statistics in education. So first of all, I would like to start with the term statistics, the basic of statistics and then gradually I will cover more and more topics under statistics in education. So the lecture will be useful for all of us. So first of all definition, look at the definition of the word, we study in education, we study in psychology, we study in sociology, we study in economics, we study in management, why do we study in all the subjects, what is the definition of the word, it is a way to manage and analyze people, students generally think that the definition of the word means that there will be a lot of maths coming in front of them, it is not like that. So there is a way to manage yourself and your eyes, if you think in a very simple way and you feel some kind of application of mathematics because you are managing your eyes, then you will connect, make a mistake, sometimes you will lose, sometimes you will lose, sometimes you will take a mistake, sometimes you will take a mistake, that is it. So don't worry about that, if you understand a lot then you will find it interesting, And since numerical based questions are asked on statistics, you can score full marks if you have done it right. So we will do a basic thing on statistics and then gradually we will cover topics based on statistics. This is very big, it has a lot of scope, it has a lot of topics and it is asked a lot. So it will take some time and then it will start on statistics. So first of all, if we look at the definition. Okay, that slide is left here, no problem. So if we look at the definition, it says that the statistics may be called as the science of counting. It is such a simplistic definition, it is so simple. They are saying that it is not something else, it is just a science of counting. In another definition, he describes it as the statistics may rightly be called the science of averages. Then they have given a lot of definitions, they have given a couple of words. In the friendly language, they say that what is the meaning of the word? You can say that it is the science of averages. That is, we talk about everything with respect, we do not talk about any individual. Then we define the statistics. Statistics is the science which deals with the methods of collecting, classifying, presenting, comparing, and interpreting numerical data collected to throw some light on any sphere of inquiry. They are saying that you have gone to do some research. So to put light on the results of the research, what do we do? We make the documents, present it, give it a summary, and we extract its meaning when the numerical data is described in the same science. So data, data collection, classification, presentation, comparison, interpretation are the statistics. Then we will go from basic to basic. We are not talking about what statistics I use, what are the plus points and what are the minus points. Just basic. You won't be able to go to the plus point. The plus point is that you can do it in a good way. You can also understand it from the diagrammatic representation that the graph is going up, down. What are the difficulties? The difficulties are that you won't talk about the individual, you will only talk about the average, we won't talk about the data that is extreme. We are going to the one where you will have to ask questions from the topics in this syllabus. So we can see that you can classify it in many ways, but we have only two ways. One is based on work and the other is based on distribution of data, that is based on the distribution of trees. So if we look at the work, then the statistics are three ways. Descriptive statistics, Coalational statistics and Inferential statistics. Children often call it statics. You don't have to say static. Static means this thing, it is called static in physics. It has a reverse dynamics, which is called gatiki. So you will say statistics, you will say it right, you will write it right, you won't do it wrong. So descriptive statistics means Varnanatmaksankhiki. Correlational statistics means Sahisamandhatmaksankhiki. And inferential statistics means Anumanik Sankhiki. Inferance means Anuman, Correlation means Sahisamandhatmaksankhiki. Descriptive means description. So in descriptive statistics, the branch which deals with the descriptions of obtained data is known as descriptive statistics. That is to say that the branch which you have summarized is described in descriptive statistics. The descriptive statistics include classification, tabulation, measures of central tendency and validity. That is to say that the central tendency is on the map. We will discuss further about the central tendency on the map, the main median mode. It is very popular, it is very popular. So in descriptive statistics, what will come? It will come on the map of central tendency and the concept of Vedata, its classification, tabulation, etc. Then what are the correlational statistics? The obtained data are disclosed for their inter-correlations in this type of statistics. For example, if you tell the inter-correlation of the data, what is the relation between the data? The statistics are called correlational. It also describes a sample or population for their further analysis to explore the significance of their differences. Apart from this, it also tells that when you read ahead, you will know that the entire population on which you are studying is a sample. It also tells the difference between the sample and the representative sample. It also tells the importance of the difference. Now, when we read ahead, you will know that the difference is actually existing because of some error. So all these things will be there. In inferential statistics, it deals with the drawing of conclusions. We are taking out the inference and telling that what will be the conclusion of this? What will be Nishkarsh? What will be Anuman? And the more we will be able to put the statistics, the more we will be able to put Anuman. So it is said that it deals with the drawing of conclusions about a large group of individuals or population based on observations of a few participants or about the events which are yet to occur based on past events. It is said that either you can tell the inference based on the sample of the population or you can tell the incident which is behind it based on the fact that it is possible to move ahead. So this is the basis of the work. Descriptive, Correlational or Inferential. Then comes your distribution of data. So these are the two main methods of parametric and non-parametric. You can call it parametric or non-parametric in Hindi or Prachalik or Prachalik. And my video is very early. Parametric or non-parametric statistics. You can read it in detail from there. So here we will just tell you the basic difference. We read the definition of Kirlinger's book and Kirlinger's research a lot. So Kirlinger said that the term population and universe, me, all the members of any well-defined class of people, events or objects. In the language of statistics, population means that all the people, events or objects that we want to control. And what is the sample? The sample is known as a part of the population that represents that particular population's properties. And its sample is called Pradarsh. Isn't it? Pradarsh or Nyadarsh. Sorry, sorry. Nyadarsh, right? We can also call it Namoona. But what is Namoona or Nyadarsh? That the population, our existence, our society, keeps all the rules, that is a good Nyadarsh. So what is the difference between parametric or non-parametric statistics? Parametric statistics refer to those statistical techniques that have been developed on the assumption that the data are of a certain type. The ancient statement of the Namoona was based on the assumption that the eyes are of a certain type. How is that? In particular, the measure should be on an interval scale and the scores should be drawn from a normal distribution. I have discussed all this with you. I have discussed the scale as well. The nominal-ordinal interval ratio is the scale. I have discussed the normal NPC as well. You can see the old one from there. They are saying that the eyes that should be in parametric statistics are of an interval scale and where we have raised them, they should be a normal distribution sample. And the normal distribution is called symmetrical spread over the continuum of minus 3 standard deviation to plus 3 standard deviation. This is a bell-shaped curve. They are saying that the central point of the beach, where the mean, median modes come, is equal to the normal distribution. So, if you take the eyes from there, then your parametric statistics will apply. And if the non-parametric statistics are not based on the assumptions of the normal distribution of the population, that is, the distribution-free statistics are also called as normal distribution. That is, the eyes from where you do not follow the normal probability sample or normal probability distribution. So, you can say that it is a little less. That is, the non-parametric will not give you as much certainty as you want. But since there is a sampling in the parametric, there is a representation, there is a normal probability sampling, then your certainty is required here. That is why the people below also call the distribution-free statistics as well. And they are not bound to be used with interval scale data or normally distributed data. And these two conditions will not be followed. That is, if the data is not of an interval scale, then it will work. Even if it is nominal, it will also work. And if it is not normally distributed, then it will work. So, you first have to know what is the nominal-ordinal interval ratio in scale, what is the normal probability distribution, what are the mean, median modes are at the same place, what are they called, what are the proper symmetries. So, you should know all these things in its background. Then you will understand what is the meaning of the non-parametric and descriptive inferential and what is the meaning of the correlational. And the statistics are nothing else. It is only to keep the eyes properly, to present, to tabulate, to compare and to analyze. It is a science. Okay. So, this is a basic understanding about statistics and now we will move forward in the statistics. Oh, how did this happen? It's not a problem. So, thank you and don't forget to like and subscribe to my channel Explore Education. I have done from my side.