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This session is about population was the sample, and what we are learning here will form the basis

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for the next session.

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It is hypothesis testing.

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So let's understand this concept clearly.

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When you're working on a mission learning project, you don't get to work on the complete data set.

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It could be due to various reasons like buying cost.

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The space may not be available.

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Part of the data may even be corrupted or any other reasons.

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So you get to work on your part of the complete data set known as a sample when you're working on a

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sample.

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What we're actually doing is you're trying to draw inferences for the population based on the inferences

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of the sample.

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Right.

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That's what you're doing, your developing machine learning model for your smaller data set, but you

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apply that machine learning model for the entire dataset.

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This population was a sample place in our day to day life also.

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Let's say you're trying to admit your kid in a school and let's say the school has got to find its students

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before admitting your kid.

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You may probably want to take feedback about the school from 20 students whom you already know.

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It is practically not possible to collect feedback from 500 students, but as it is possible to collect

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feedback from 20 students, if you see here, if all the grindy students give a good feedback out of

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the majority of the students, give a good feedback, I may probably end up admitting my kid in that

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particular school.

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So what am I doing here?

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I am extrapolating the inference that I got from my sample on to the entire population.

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I'm saying whatever inference are drawn for the sample is applicable for the population as well.

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Right now, to ensure that whatever I have drawn as insurance, for example, is applicable for the

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population, I must ensure that this sample is representative.

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So what is this representativeness if you see this example that is there on your screen?

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There are different types of students in the population and there are different types of students in

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the sample.

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OK, now if the percentage of different types of students is maintained in the sample, also, if the

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same level of percentage is maintained in my sample, then the sample is said to be representative of

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the population.

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In this scenario, this 43, 22, 17 and 17 is not reflected in the sample, the percentage contribution

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of different types of students is different from the members or the percentage that you see here.

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Hence, the sample is not representative.

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There's a very, very important concept in machine learning or deploying.

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Why do you think this is applicable, reflecting your developing a machine learning model to predict

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how diseases and if the majority of your dataset comprises of Indians and you go and apply the model

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that you've developed with the majority of Indians in your database to, let's say, Chinese or Caucasians,

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your inference wouldn't be accurate enough because the propensity for heart disease is much higher in

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Indians and Asians compared to Chinese and Caucasians.

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Right, your model will be accurate onely, if the percentage contribution of different races is addressed

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in your sample.

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Are you understanding why this concept is relevant when it comes to machine learning and deep learning?

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OK.

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The same concept of representative sampling is important in the world of opinion polls, also, if let's

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say you're trying to assess the likelihood of a particular party winning the elections and see us or

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India, you must ensure that your sample is representative of the population of the electorate.

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If you're going to collect more feedback from, say, northern or central India, your conclusion or

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inference won't be comprehensive or accurate.

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The same holds true for the US also.

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If your feedback is primarily from coastal areas, right, and you ignore subtle and mysterious, your

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inference wouldn't be accurate because your sample is not representative of the population of electorates.

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So please keep this in mind whenever you're working on samples.

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The other aspect when it comes to sampling is what is known as random sampling.

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Random sampling ensures that there are no biases when it comes to picking the sample for taking feedback

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or doing any type of study in the example that you see on your screen, that is one a local or one red

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color one, one blue color one and two green colored balls.

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Now, the blue colored ball, I have taken the ball that is number six instead of six.

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I could have picked up an eight or 11, nine, 14 or 16 or 17.

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Each of these blue balls have an equal chance of being picked up the sample.

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If I ensure that each of the blue balls have an equal chance of being picked up, then I have short

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randomness in the sampling.

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Now, let's try to understand this, using the school example, there are 500 students and I am taking

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feedback from 20 students.

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What if I collect feedback from 20 students?

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I know very well I'm introducing a bias, right.

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So when it comes to sampling, let's ensure the randomness is maintained and representativeness is also

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maintained, only then the model that you develop will be accurate, otherwise you will have lower accuracy.

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OK.