 Hello, I welcome you all once again to my channel Explore Education. I am Dr. Rashmi Singh, Assistant Professor, Department of Education, S.S. Khanna Girls Rumi College, University of Allahabad. And today I am going to discuss a topic under the heading of statistics, statistics. In statistics there are several terms which we have to know about the types of statistics that is descriptive and inferential statistics. So today we are going to discuss the difference between descriptive and inferential statistics. Lecture will be in bilingual mode and will be useful for various teaching examinations and your basic conceptual understanding about the statistics as a discipline. Okay. So, when we read Sankhiki, there are many terms that are used repeatedly. Statistics, descriptive statistics, inferential statistics, population, sample. So, before we start what are the statistics, population, sample, descriptive and inferential we know and then we know the difference between the two. Because, it is important to ask the forum and solve the questions. And conceptual understanding is important so that we can understand what we are doing and why it is like this. If we get to know the reason, then many things will be easy for us. So, first of all we know what are the statistics. Statistic, statistics is the area of applied math that deals with the collection, organization, analysis, interpretation and presentation of data. See, a lot of students feel that what statistics are is math and those children whose numerical competition is not good, they say that they get scared that statistics means math. Statistics is not math, it is just applied math. It also contains a few parts. The whole statistics is not numerical based, there is a lot of theoretical in it. You have to understand it, you have to understand it, there is an interpretation. There is a little part where you need to get scared. So, you don't have to be afraid of it. Statistics is the area of applied math. Means, there is a lot of use of mathematics that deals with it. Organize, organize, analysis, visualization, interpretation. We have to take out its meaning and presentation. Means, we have to present it, to whom? to the data. Means, to whom all the work is going on? to the eyes. How can we present our eyes in a better way? By applying statistics, so that its meaning also comes out. And what is it? Statistics is defined as the process of collection of data, classifying data, representing the data for easy interpretation and further analysis of data. Means, what is all this being done? So that we can do its easy interpretation. Means, we can easily tell its earth. We can give earth to the scattered data, we can give it a definition. And further analysis of it can be done. And if we define it as statistics, then in the words of Welles and Roberts, statistics is the body of methods for making wise decisions on the age of uncertainty. Means, if we can take wise decisions in the era of uncertainty, then those are the statistics or we get help from the statistics. And Albogli says that statistics as the science of counting. Again, he defined statistics as the science of averages. What do we often say? We take out the mark, the mean, the median mode, standard deviation. Who is talking about the mark? So, the mark is the science of the medium. Or the science of the age. What are the statistics? Then a basic understanding of statistics is that if we want to know who the father of statistics is called, and who is the Indian father of modern statistics in India. So, Sir, Ronald Elmer Fisher, renowned as his time's greatest scientist, was a British statistician and biologist who made significant contributions to experimental design and population genetics. He is widely regarded as the father of modern statistics and experimental design. That is, R.A. Fisher, generally they know him as R.A. Fisher. So, R.A. Fisher, he is called the father of modern statistics and experimental design. And he has a lot of work on the statistics, ANOVA, null hypothesis, and everything that he did. And you know how popular those statistics are. And if you look at the Indian and Indian studies, then PC Mahalanobis is called the father of modern statistics in India for its various contributions in the field of statistics. So, this is a basic information about these statistics. Now, what we will use again and again, is Population and Sample. Generally, we consider the population as the country's population. What is the status of the country? What is the status of the population in statistics? It is the entire group that you wish to draw data from. That is, the whole community through which we want to be able to unite with it, that is the status of the population. And subsequently, draw conclusions about it and about which we can draw conclusions. While in day-to-day life, the word is often used to describe groups of people such as the population of a country. In statistics, it can apply to any group from which you will collect information, right? That is, sorry, any group in the field through which we want to unite data, it will go to our population if we are taking it in totality. And what is the sample? That is, its sample. That is, how its sample is representative sample, representative group. That is, it is the sample that keeps all the population in motion. A sample is a representative group of a larger population. Random sampling from representative groups allows us to draw broad conclusions about an overall population. This saves time, hassle, and the expense of extracting data from an entire population, which for all practical purposes is usually impossible, right? That is, the sample that is the sample is extracted from the population. It is extracted from the population. Generally, it is extracted from random sampling representative group. And it keeps all the population in motion. That is why we apply our statistics on the sample to generalize the results of the population, which is our goal, which is our goal. Because studying the entire population is almost impossible. Some will come like this, some will leave like this. That is why you cannot study the entire population. That is why we have to extract samples from it. So, this is basic understanding. Now let's talk about the main topic. What are descriptive statistics and inferential statistics? What is it? So, what are descriptive statistics? It focuses on summarizing the key features of a data set. That is, the main purpose of Varnanatmaksankhiki and its focus is that we can extract the key features of any data set. We can tell the characteristics of it. The term descriptive statistics can be used to describe both individual quantitative observations as well as the overall process of obtaining insights from these data. That is, we will also tell it in Matharatmakhrup and in Gunatmakhrup. Insights can also be presented to it and we will also tell it quantitatively what that data set is. Because they are merely explanatory. Descriptive statistics are not heavily concerned with the differences between the two types of data. That is, the difference between the two different types of eyes is not explained in the descriptive statistics. What is this? It is explained and Varnanatmakshiki and the data set is given to it. It is given to its own. And it describes a sample that is pretty straightforward. I mean, it is straight, there is no turning back. You simply take a group that you are interested in. That is, we first take a group in which we are interested, in which we want to collect data. Then what do we do? We record data about the group members. We record data from the members of that group. And then use summary statistics. And then we put summary statistics on it. For example, we presented it with a graph of how many boys and girls or how many boys and girls are of this height weight. Or according to our intelligence, we have done some categorization. It can be graph, pie chart, or any mode of representation. With descriptive statistics, there is no uncertainty. This is special. There is no uncertainty in Varnanatmakshiki. Why? Because you are describing only the people and items that you actually measure. I mean, we have actually studied people, actually put statistics on them. We are writing about them. So why are we uncertain? We are not sure. We are absolutely sure. We are absolutely sure that the result comes in the company of pureness. You are not trying to infer properties about a larger population. Here we are not going to infer any big births. We are not putting any overall population. Neel Bhai, inferential statistics focuses on making generalizations about a larger population based on a representative sample of that population. Look, in the descriptive, we have studied the entire sample and we have taken out the result with certainty. But what do we do in inferential statistics? There is a large population, a large group where we can't study the entire sample, we take out the representative sample. So when we have taken the sample representative, why would the results be generalized? Why would the results be generalized? Because inferential statistics focuses on making predictions. We don't make predictions. Its results are usually in the form of a probability. That is why half of the results are taken out. That is the probability that you will do this. And it takes data from a sample and make inferences about the larger population from which the sample was drawn. This is clear that we have to take out the sample from the population and we will generalize the results of the results on the entire population. And this is what we draw inferences from. This is how much we can draw inferences from. Because the goal of inferential statistics is to draw a conclusion from a sample and generalize them to a population, we need to have confidence that our sample accurately reflects the population. So what is the whole goal here? That the error should be at least the chance should not be at least the measurement should not be at least and we should be able to give our probability statement of the probability that we should be able to draw inferences that we should be able to make predictions. Okay? Now let us know with the help of the table that what is in the descriptive and inferential statistics. What are descriptive statistics? Features of population and our sample. The population can't be used. If there is a small population, it will happen or the population will be sampled. But what is the main goal? Sorry. All the members of the group will study and draw inferences from all of you. They will give a complete certainty. And here... Sorry. In inferential statistics, there are huge samples to make generalizations about larger populations. So here, we study the whole population and represent it on the sample. Here, in the descriptive, organized and present data is purely factual. It is factual. But either helps us to make establish and predict future outcomes. We will have to predict something. We will have to tell it in terms of probability. This is what is coming out. Present final results, visually using tables, charts and graphs. Here, you will get all the descriptive statistics. You will get the table, the graph, but what will you get in inferential? You will get probability. Then in the descriptive, draw conclusions based on known data. You know the data. But what happens in inferential? Draw conclusions that go beyond the available data. Because the sample is not the data. The data is the population. Now, it depends on the sampling. If you have done the wrong sampling, then you will lose your book. And here, what are the statistical techniques in the descriptive? Measures like central elements, like mean, medium, more, distribution, range, variance, etc. When in inferential, use techniques like hypothesis testing, confidence intervals and regression and correlation analysis. Okay. What happens in the descriptive? For descriptive statistics, that is, choose a group that we want to describe and then measure all subjects in that group. The statistical summary describes this group with complete certainty. It has been said that there will be no measurement error here. In inferential, we need to define the population and then revise the sampling plan. Then that produces a representative sample. The statistical results incorporate the uncertainty that is inherent in using a sample to understand an entire population. The first step is to choose a representative sample from the population. If you start wrong from there, then your uncertainty will increase. Then, what happens in the descriptive is that it helps in organizing, analyzing and representing data in a meaningful manner. That is, the earth is becoming crumbly. But in inferential, it allows us to compare data and make hypotheses in prediction. Here, you can only prediction, generalization, inference, because you don't study the population. And the error should not be made. It is used to describe a situation. It is used to describe a situation when in inferential, it is used to explain the chance of occurrence of an event. What is the chance of any event? It is the result of probability of the terms. In the descriptive, it explains already known data is limited to a sample or a population having a small size. The size of a small size can be a population or a sample study is limited. When in inferential, it tends to reach the conclusion about the population, which I have not studied. In the descriptive, it can be achieved with the help of chart, graph, table. Then descriptive statistics is that branch of statistics that is concerned with describing the population on the study. When inferential statistics is a branch that focuses on prime conclusions about the population on the basis of sample analysis and observation. Either organize, sorry, the point has been repeated organize, analyze and present data in a meaningful way or either compare, test and tweak data. And last but not the least, it explains the data which is already known to summarize. The data we know from the beginning we have summarized it, organized it, analyzed it, in the descriptive. When in inferential, it tends to reach the conclusion to learn about the population that extends beyond the data available. The data of the sample is bigger than the data on which you have to apply your result to which you have to generalize your findings. Okay? So, basic answer, I think, the descriptive or inferential in the descriptive are the results of central tendency of statistics like the hypothesis testing or regression analysis are going on in inferential. Okay? So, basic understanding is very important. Before starting any topic, there are many topics in the statistics to explain and understand. But for now, basic understanding, what is the difference between descriptive and inferential? So, in the descriptive and inferential statistics, and I hope that you have understood. So, thank you and don't forget to like and subscribe my channel Explore Education. Okay? Then from my site.