 Welcome back. We'll start this session. So have with me Professor Sachin Patwardhan. So Sachin, you would have seen the videos in scientific methodology and in reading. We will take up, Sachin will first take you through important points from the scientific methodology presentation. Then we'll follow it up with small activities and discussion and then we will go on to the modal assignment that you have all submitted and we'll take up examples from there. So good morning. So I'm going to run you through this presentation on scientific method. You must have seen this already through modal. So let's begin with the definition of the scientific method. A method or procedure that has characterized natural sciences since 17th century. Now there are some highlighted words which are very, very important. Consisting in systematic observation, measurement and experiment, very crucial. And the second part which is crucial is formulation, testing and modification of hypothesis. I think since you have already done some exercise regarding proposing a hypothesis, now this definition will probably make more sense to you. A critical thing is we have to propose a hypothesis, a causative mechanism that explains the observations. Now this method builds on the previously available knowledge. You are not doing something completely independently. You are trying to build upon, you are trying to use prior knowledge already available in the field. It continuously improves our understanding of the world and more importantly, this scientific method, it improves itself as we keep applying it. And the most important, most crucial aspect of the scientific method is falsifiability or refitability or testability of a hypothesis or a theory. So this is one of the most crucial and important steps. And we need to understand this step if we want to explain this to other participants and to students. So it is important that first we should formulate a question, the scientific question that we are investigating and then we should develop a hypothesis or a conjuncture or a causative, a fundamental causative mechanism which not only explains the observation but which can also explain some other observations which can be made independently. That is what is related to this testability or falsifiability and it should be done in a meaningful way. The test should be a meaningful test and everyone should be able to conduct this test. So we carry out predictions based on this fundamental causative mechanism and then for these predictions, we can actually test the predictions. If the predictions hold then we accept the hypothesis and proceed further. Now the steps involved are we conduct the experiments to test or evaluate the predictions. Then we analyze the results of the experiments and the last step is very, very important. It is not that we keep these results to ourselves. We communicate the results for the peer review and for dissemination. In a way, we are going to do that today. We are going to look at the assignments that you have submitted and we are going to be the peer reviewers for your assignments. So you do not do this in isolation. You actually communicate the results of your experiments and you get feedback from the peers to understand whether, you know, the way you applied is correct or wrong, whether it is acceptable or not acceptable. So this communication or the technical communication in which you communicate your result is a crucial step when it comes to the scientific method. Let us quickly move on through the example, a famous example I have given in a lecture, online lecture. So this is about the atom. The structure of atom was at the center of scientific inquiry at the end of the 19th century and we all know about this famous experiment that was conducted by J.J. Thompson. So this was involving electric charge discharge to different gases and through these experiments, it was revealed that there are fundamental particles or fundamental entities called atoms which contain negatively charged particles. So at that point, they were not named as electrons and these particles are identical in all the atoms. So what are also known from the prior knowledge was that the atoms are electrically neutral. So how do you explain overall electrical neutrality of the atom? That was the main question that was being discussed. So of course it relates to the structure of the atom. So one has to hypothesize existence of positive charges or the protons to nullify the electrical charge and to prove electrical neutrality of the atom. But then the question is how are protons and electrons arranged in an atom or in other words, what is the structure of an atom? So it was hypothesized that the atoms can be regarded as spheres of approximately 10 nanometer radius and carrying equal number of protons and electrons. So this famous hypothesis of plum pudding or blueberry muffin which we now study in school textbooks is where the atom is visualized as a pudding of protons in which negatively charged raisins or electrons are floating. So this model or the plum pudding model could explain electrical neutrality of the atom. The mass, the hypothesis that was underlying this particular model, atomic model was the mass of protons is evenly spread over the atom and electrons are free to rotate within the cloud of the positive substance formed by this atom. So this very, very successfully explained the electrical neutrality of the atom and but did it explain everything that was known about the atom? At the same time what was known that different elements have what are called as spectral lines. So can we explain the phenomena of spectral lines, observed for different elements using the structure of the atom proposed by Thomson? The other thing that it could not explain was Geiger and Marsden's Goldfoil experiment which was conducted in 1919. So this experiment it consisted of bombarding a Goldfoil with alpha particles, positively charged particles and then it was expected that if the model proposed by Thomson was correct then all the alpha particles should pass through the Goldfoil without any deflection. But Geiger and Marsden's experiment showed that a small fraction of alpha particles actually deflected and very, very strongly as the angle of deflection was as large as 150 degrees. So how do you explain this? So the plum pudding model, the hypothesis that the positive charges are spread like a pudding or a muffin could not explain this particular behavior. So an alternate hypothesis had to be advanced to explain this behavior. So in 1994, in 1904 Japanese physicist Nagaoka had come up with a model which was similar to the solar system model, the solar system. So all the positive charges were supposed to be gathered in a nucleus like sun and electrons were hypothesized to obite around the nucleus like the planets that move around the sun. Or you could view it like the Saturn rings where the planets are moving around Saturn which is the nucleus and the planets of Saturn are moving around the nucleus which is Saturn. So this particular model Rutherford found interesting and he developed a mathematical theory that could explain the behavior of alpha particles that was observed in Geiger-Marsden experiment. So similar to the plum pudding model, it was assumed that the electrons are rotating around a nucleus which consists of only the positive charges. He not only used this model, he came up with an expression that could actually explain the scattering of alpha particles as a function of angle and also which relates to the material of the foil which is used. So this particular experiment had produced the data of scattering versus angle and the model could predict the scattering versus angle. And what was amazing was that the model could explain to a great extent the observed behavior. You can see this from this particular figure that the model proposed by Thompson could not do this whereas the Rutherford's model could explain behavior of alpha particle deflection. So later few more experiments further confirmed the predictions of Rutherford models. So Rutherford model predicted certain behavior of alpha particles and it was exactly confirmed by the experimental observations. So this is one of the famous examples of the scientific method at work. We now move and talk to you about a specific example which is from day to day life. So is it that we apply this example, we apply this method only for science? We can apply this method for everything that happens around us. It's an approach to come up with a causative mechanism to come up with a theory to explain certain observed behavior. So I'm going to actually ask you to do a short exercise based on the very example that we have discussed in this presentation. But before that let me just sum up the steps, main steps in the scientific method. First of all we have to have an observation, some interesting observation when it comes to science or in day to day life there might be some other observation which we need to explain. Of course there should be some measurements possible for this observation. Then to explain this observation we come up with a hypothesis. A hypothesis is a fundamental explanation, fundamental theoretical explanation or a fundamental causative mechanism that not only explains this observation but can possibly explain some other observations, some other phenomena. So we do predictions using this hypothesis, using this causative mechanism. What else you can predict using the hypothesis that you have made? If we come up with these predictions we can validate the hypothesis by testing our predictions. And if the test is positive then we accept the hypothesis, the test is negative, we go back and revise the hypothesis. We have to change the hypothesis, come up with a new hypothesis. So this is the testability of a hypothesis. This is an example which I am going to run through very quickly but we want you to come up with alternate hypothesis to explain this particular observation. Observation here is that the computer monitor display is blank. So one could come up with a simple hypothesis in the beginning that is a general power failure. So I entered this room and the computer monitor display is blank. So with this hypothesis I can come up with a prediction that if there is general power failure then all other power equipment in this room should be down. So what is the test that you can come up with? If my prediction is that all other equipment are down I should test whether the equipment are down. So I can switch on the other equipment and check, I can switch on the bulb, I can switch on any other power device in my room and check whether it is working or not. So this is a test for a prediction which is based on my hypothesis. This test can be anyone can test this. It is not necessary that you need any special training. And well if I switch on some other equipment and it is the equipment turns on then my hypothesis is false because some other equipment let us say light bulb is switching on. The light bulb is switching on obviously all the powered equipment are not down. So I have to come up with some other way of explaining. I have to revise my hypothesis and then we have rehypothesized here that equipments connect to a particular phase are down. We have to come up with a test for this hypothesis. If this particular hypothesis again is found false then we have to come up with a new hypothesis and this goes on till we find that hypothesis that we have made is true. So this is a iterative process in which we come up with a hypothesis, we come up with a prediction that can be made using the hypothesis an alternate prediction that can be made using hypothesis. We test it and then if the test fails then we revise the hypothesis. If it does not fail we accept the hypothesis and go forward. Okay so what we will do now is we will take the same example as was shown that the displaced failure and as I said before we are going to carry out this activity. First you will have to do something individually and then after about couple of minutes you have to share with your neighbor so if you are not sitting next to another person. So please pair up, please reorganize your seat so that you can sit next to another person. So what we will do now is each of you have to come up with a hypothesis not the one which is given here. So two examples were discussed here one is a general power failure and other is at least one phase failure. Now let us say none of this hypothesis were true. So you have to come up with another hypothesis for which explains the failure of the display. So please write that down and also write down a prediction which you can test. Alright so first what you will do is please write down a hypothesis and a prediction on the book that you have and I will give a queue after about couple of minutes and then you have to share your answer with your partner. Listen to their answers and then criticize or accept their answers and then we will randomly pick up a center which is ready to participate and then we will ask you to present to the whole class across the country. Okay so let's start now. Take about two minutes. Think of a hypothesis and a prediction that can be tested. Okay so what we will do now is to take some questions. So I am going to start with Sarvajanik College of Engineering. Observation is computer monitor is blank. Yes. Hypothesis is power cable failure. Okay so I am just repeating for the benefit of others. The hypothesis is a cable failure. Alright so if cable failure is true what is the prediction? What else can happen? The replacement with another fresh cable should work. Okay so use another fresh cable. Now use another fresh cable is not a prediction. So I would like everybody to please sit down. So this is a point which many of us have difficulty in understanding. What we have, what we need is a prediction. I will take your question please wait. What we need is a prediction. Now prediction is something else that will happen if cable has a failure or cable has been cut. Okay change the cable is an action you take that is not a prediction. Okay change the cable is not a prediction. So you have to come up. So I am sure many people will have a similar kind of argument. We often confuse what to do with what is a prediction. Most of us come up with solutions do this do this and it will fix that's not what we want. Of course that is the final thing that we will come to but that has to come through a sequence of steps. Correct prediction that wall replacement with another cable should work. That is a prediction. No that is not a prediction. That is a solution. A prediction is also an observation like for example in the example that was cited. Okay general power failure. Okay was the hypothesis. So a prediction is something that you should answer if there is a general power failure then definitely the monitor display will go blank. This hypothesis explains this observation. Now this hypothesis has to also explain some other observation. What is that observation? That observation is called as a prediction. A second observation which has the same causative mechanism. Which has the same causative mechanism is called as a prediction. We are not asking for solutions. Most of you come up with solutions. Solutions come later. We are trying to first of all you don't even know if what you are hypothesizing is true. Only if you know the hypothesis is true will you need a solution. At the moment we are not even sure if our hypothesis is true. Okay so let me take it in another center. Hello. Will I answer to you? The hypothesis is display itself has some problem. The monitor itself has some technical problem. The prediction is that put the display in some other electrical plug point. If there is a problem. Again same situation. What you are proposing is a test. Okay. It is not a prediction. If you say that a display itself has problem. Say let's say the monitor the LCD screen has got some failure. If the prediction is you have to answer a question. If LCD screen has a problem what else can happen? Please write this down. All of you will give another 5 minutes to come up with new predictions. You have to write down a sentence what else is true if hypothesis is true. What else can hypothesis lead to? Alright the causative mechanism can lead to several things. One of them is display failure. The same causative mechanism can also lead to another thing. What is that thing? Alright. So if that happens to be testable as what was told in the lecture. You need to make you need to be able to test it so that there is a possibility that you get a false answer as well. So what is that another observation that has the same that can have the same causative mechanism. So we will take another couple of minutes. Again we will do the same exercise think for yourself first write it down. And then share it with your neighbor and now when your neighbor presents to you. You have to be critical the way we were critical of you. You cannot provide a solution as a prediction a solution is different. A prediction is having the same causative mechanism but another possible observation. So let's take another 2 minutes and write for yourself a prediction which has the same causative mechanism as your hypothesis. Okay. So now about time that you need to discuss with your partners. So once I hope you have come up with a prediction which has the same causative mechanism as your hypothesis. So please present it to your partner. To say about the hypothesis it was thought about power cable is loose. Power cable connected to the switch button is loose. Okay. The prediction which was thought about was power cable was not fitted properly in the switch. The test suggested is the power cable with other device to be tested. Okay. Or fitted properly. So I will again go through this question. So what she said was the power cable was loose and the prediction was check the power cable or something of that sort. Now again that is not a prediction. You reformulate the sentence as follows. If power cable is loose the prediction is check the power cable. It doesn't make sense. It is not a prediction. A prediction is another observation. Okay. If the power cable is loose what else can happen? Check the power cable is an action. It is not a prediction. See what we are saying is the final answer what you got may be right. You are analyzing the problem correctly. That's not the point. The point is is it a prediction? If the power cable is loose what else can happen? Sir we can predict that it is not fitted properly. It is you are trying to solve the problem. We don't want to want you to solve the problem at this moment. At this moment you have to first of all figure out if the hypothesis is true. Okay. So I have a suggestion from the audience here. The prediction says it could be a visual inspection of the cable. Okay. It shows that it is loose. That is one possibility. Or if you use the cable in another computer and put it firmly that display should work. Okay. So that is a prediction. Something else should happen because the cable is loose. Okay. So I will take with another university here. This is Geetham University Hyderabad. Hello. Hello. Yeah please go ahead. Good morning sir. The display failure is an example. So the observation here is computer monitor display is blank. The hypothesis is monitor display has failed working. Has failed? Prediction has failed working. That is your observation. The monitor display has failed. Okay. Display has failed. Okay. This is the observation. Display has failed working. The prediction is the CPU should work with other sorry CPU should work if connected with other monitor. Excellent. So the test to be done is. No that is okay. Just let us stop for the moment. Let us discuss this point. So first we have given a very good hypothesis and prediction for the benefit of others. I am going to repeat it. Okay. And I am going to repeat it slowly. So the display was not working. Okay. Now her hypothesis is that the display the monitor itself has failed. Okay. Now if the monitor has failed then if you connect the CPU to another monitor which is not working then you should be able to see the computer output. Okay. So this goes to show that your hypothesis is true. Okay. Now if you go and do that test and still you see that another display works then you know that your first hypothesis was true. And if you go and see another monitor also does not work then you have to discard the whole hypothesis and go back and rehypothesize. Okay. A very good Gita University. We will go to Samaya College of Engineering. Hypothesis is computer screen is blank because of some computer software problem because of some virus. Prediction is all files have got corrupted. They see run the antivirus scan and found the virus. Okay. Okay. So let's just stick only with the prediction for the moment. So your prediction is all the files will be corrupted because the origin of the problem was the computer virus was causing the display failure. Correct. Your computer virus was causing the display failure. So the prediction is all files would be corrupted. Now while it seems okay, so first of all we need to look into the actual problem whether it is at all possible that a display is failing because of a computer infection. Can that happen at all first? Most of the time some professors are running and temporary. Okay. There is one more issue here. See in my presentation I said the hypothesis should be testable. Okay. So now if the display is not on, how will you test the prediction? The prediction should be testable. The display is not on. How will I test that the virus is there and other files are also corrupted? The testability is failing here. You understand? If the display is not on, it is not possible to test that the files are corrupt. Okay. Thank you. Let's go to another college. VNR Vigyan Jyoti Institute of Engineering. Okay. Vigyan Jyoti, you have only audio. We can't see your video. So if you can hear us, please come up with a hypothesis. Okay. Yeah. Okay. The hypothesis is the system is like the observation is the computer monitor display has gone blank. Hypothesis, the system is not properly connected to the power system and prediction is the plug point must not be working. See the hypothesis is okay. Hypothesis first of all explains the observation. That is very important. First of all, does the hypothesis explain the observation? That is important. In your case, it does explain the observation that the cable is not connected properly. The power cable is not connected properly. Now, if you're saying the power cable is not connected properly, then it does not lead to saying that the prediction is not the plug is not proper. That's not a prediction. You get? If I write the prediction like the cable wire is old and a non-functional one, would that be correct? No, that is not a prediction. You see a prediction means, okay. See, let me just take this picture. Let's flip the same example and let's make a hypothesis that the plug point is defective instead of making it as a test. So let's make this as a hypothesis that the plug point is faulty. Then I can come up with a prediction that other devices connected to the plug point should also not switch on. And I can come up with a test. See, there might be a printer connected to my plug point. I can say the printer also should not be turned on if I switch on the light. If I switch on the power button of the printer. So your hypothesis, if you make a hypothesis that the plug point is defective, this causative mechanism that the plug point is defective will explain some other behavior that the printer cannot be turned on and you can test it. Come back to this point. If you can see my slides here, the prediction, observation are the same class, okay? A prediction is also a possible observation. The hypothesis common between a prediction and observation is the causative mechanism. The hypothesis leads to observation. The hypothesis also leads to prediction. So the observation and prediction are the same class. A prediction is also an observation which is not yet done but you are going to do, okay? Now this explains this for sure because that's your hypothesis. Now this one has also need to explain something else and that is the prediction. Many of the examples that people have stated here are not predictions. They are probably something like a test or solving the problem. But all comes much later. You test it and solve the problem only if the hypothesis is true. For hypothesis is true, you need to find an alternate observation and that alternate observation is what we are calling it as prediction. Now if this alternate observation is also true, only then you can gain confidence that okay, my hypothesis of this explaining this is correct, okay? So we have about 15-20 minutes left and in this, we will again post lunch session, we will take this up again in much more detail with the examples that we have presented in Moodle. I'll just quickly just look at some of the hypothesis that we discussed just now. See Sarvajani College proposed a hypothesis that a cable has failed. So one could come up with a prediction that if this cable is used to power another device, another monitor, then the other monitor will not switch on. So this could be my prediction. So I'm trying to predict behavior of another device, which has the same causative mechanism that is this power cable, okay? And then of course I can perform the test. I can actually connect this power cable to another monitor and check. So you have to come up with a prediction, an alternate observation which has the same causative mechanism. Causative mechanism is failed power cable, okay? See, let's go back to the Atom example, the famous example, okay? Now both the models, Thompson's model and Rutherford model, both explained electrical neutrality, okay? One predicted that alpha particles should pass through without any deflection. The other predicted alpha particles should pass through deflection, okay? Now when the test was conducted with the alpha particles, only the predictions made by Rutherford model could confirm with what was observed. That is why the hypothesis of plum pudding model was rejected, okay? So you have to come up with a test, an independent observation, okay? With power cable failure as a hypothesis, I can come up with a prediction that with this power cable, another monitor will not be powered. So this is my prediction. And then I come up with a test of changing power cable of another monitor and replacing with my hypothesized failed cable and check whether the other monitor is switching on. If it switches on, I have to change my hypothesis that the cable has failed. If it doesn't switch on, maybe my hypothesis is correct. So another college had come up with a hypothesis that the display itself was a problem. Correct? Now when the, suppose the hardware of the display, the LCD monitor, the hardware of the LCD monitor itself, something has gone wrong inside, okay? That is the hypothesis that something has gone wrong inside the hardware of the display monitor. Then if that is the causative mechanism, if you take this monitor and put it in other computer where the display works, then you should not see any display. That is a prediction. That is also a second observation. Like in this picture, the hypothesis was the hardware of the LCD monitor has failed. Now the hardware of LCD is failed. That definitely explains your observation that there is no display. But what else it can explain? It can explain if I take this display and it is connected to a working monitor, a working computer, even there you should not see a display, alright? So when you don't see a display there, so that test can be performed. The test and all those things come later. But first of all you need to make sure that your hypothesis is at least correct in a second situation. If your hypothesis is not even correct in a second situation, then it is like, I'll take an example of, many of you might have done PhD. So in the first year, you think that a particular explanation is the correct thing and you are doing lot of things around that. But if that hypothesis itself is wrong, you are spending two years around that, writing a big paper and sending it, where the hypothesis itself is wrong. So similarly in this case, unless you make sure that at least in a second situation, the hypothesis works, then you get some confidence in it, okay? So the second situation is if, so the prediction is like this, if the hardware fails, okay, then connecting to a working computer, there should be no display. So this is prediction. I'm telling something that will happen in future, which I have not yet done. This is an observation. A prediction is an observation that is in future. You predict something what is going to happen, if this was the causative mechanism, that is not yet happened, okay? That is why it is called as a prediction. You're predicting something will happen in future if you do this. And then when you do the test, you can get both answer, yes or no. So if the answer was no, you have to go back and re-hypothesise, okay? So talk to your partners about this over lunch. We will, we have another 10 minutes. So in 10 minutes, we'll just quickly run through this steps in technical communication and scientific hypothesis. And then we'll come back in the afternoon session and discuss the non-arrival of bus, which we have all done in Moodle. Okay, so let's go over through the importance of technical communication very briefly. So the review or the criticism of the hypothesis, actually what we are doing with your live right now of a model or a theory by the scientific community, by the people who are experienced in doing so, is an extremely important step in advancement of science or advancement of scientific thinking. So review by or criticism by peers, the peers are acknowledged experts in a particular area. Of course, we are just right now demonstrating through a very simple example, but you know in your respective fields, when you communicate a paper, a conference paper or a journal paper, you get review comments by experts. And this is a very, very important step when it comes to scientific, you know, development of science and technology. We learn through collective feedback, you know. Now is it that only peers are allowed to criticize? It's not the case, anyone can criticize. Actually, the crux of scientific method is that anyone who can give a logical, you know, support to his or her argument can criticize a theory, a hypothesis or a model. So even if you are a budding researcher, even if you are a no voice, in fact, you might think in a different way and you might be able to come up with a better criticism than peers at times. So this part, criticism or getting reviewed is one of the most important or critical steps. So publishing your results of your scientific investigation, making them public in some form is very, very important. It could be a technical report, it could be a project report, it could be a journal or conference article, it could be your master's thesis, it could be your PhD dissertation. All these have to be subjected to peer review and this is very, very important. So most of us believe that science and technology progresses through some, you know, brilliant effort of some well-known scientist. Well, that is only part of the truth. It produces good, I mean, we produce good science or good technology through this collective feedback mechanism in which each one of us learn to present our results of investigation to the peers, peers criticize and then we correct. And then because we correct and reformulate our hypothesis, our explanations, the scientific document progresses or it becomes better and better. So that happens when you submit a paper to a journal for review. You get review comments based on that, you change your paper draft then again you send for review, again you get comments and finally after two or three revisions it gets accepted. This is very, very important part of the technical communication. So what are the steps in technical communication? You have an interesting observation, you communicate. You may have already observations recorded in the literature and somebody has given a causative mechanism, you do not agree with it. You come up with a new hypothesis to explain the observation but with this particular hypothesis you can predict some other behavior, show that device a test to test your predictions and then you communicate your results. This maybe it is accepted and published, somebody else comes up with a new hypothesis, revises the theory, revises the hypothesis and then publishes his or her own results. So this process goes on and this is how the science and technology develops. So it is important to know that it is not a do-it-yourself recipe, it is an interactive iterative process which requires knowledge, skill, intelligence and creativity. So the most important, the first step is to define the question. Then you gather information, you gather literature, you find out what others have done to explain a similar behavior. Even in fact, even when you come up with a hypothesis for a simple observation like the monitor has failed, you have drawn upon indirectly, unknowingly drawn upon the literature or what you already know your prior knowledge about this. Then you evaluate research options and formulate a hypothesis based on your prior knowledge and also maybe you may come up with a creative new explanation. Then you perform the experiment, gather data to test your hypothesis or test the predictions of your hypothesis, analyze and interpret the data to evaluate the hypothesis and then publish the evaluated hypothesis through any one of the accepted scientific platforms like conferences, like journals, like reports, like patents and so on. Now somebody may disagree with your hypothesis, your theory, you come up with a new hypothesis, a new model. This is typically done by somebody else and the process goes on. So this is just briefly, briefly a quick review of how technical communication plays a very, very critical role in development of a scientific theory or a model. And then actually we are doing a very, very short, simple run-through of that. Criticism, you are proposing a hypothesis, we are criticizing the hypothesis. We are trying to show what should be a proper way of defining a hypothesis, what should be a test and so on. Break now for lunch and we will come back here at 1.15 and we will continue this session on scientific method and we will take up examples from the Moodle assignment that you have submitted and we will see what are good ones and what are the things that needs improvement. Goodbye then for now.