Tutorial 33- P Value,T test, Correlation Implementation with Python- Hypothesis Testing 

It's really true

Height and age doesn't follow Poisson distribution, it follows normal distribution.

yeah, and the output shown is "We are accepting null Hypothesis"

We fail to reject the null hypothesis, because it is > 5%. 74% >5%

@@swarajkumarsahoo4736 he didn't run that cell at all. He ran previously means not the n video. So that output is wrong I guess and what he said is also wrong

Yes me to iam confused , did he make a mistake?

Shrikant Agrawal please answer this Krish as it makes sense for all of us

Thanks bro

Same here

Iam also confused

Please correct guys Accepting the Alternative hypothesis and rejecting the null hypothesis is the correct answer. Mistakes will happen sometimes😊 I am also a learner..👍

0

I also got the same doubt got any answer?

I also ....☹️.....and how can we take those mu values ..?? Plz anyone clear my doubt

Nope didn't get it yet. If someone gets this doubt clear then please tell.

ya same doubt i think mu is possion parameter

@@kiranchowdary8100 You are right. It is some parameter. You can check it here: docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.stats.poisson.html

@madhav srimohan..did u got this?

@@mishuchugh1777 did u got this?

Have you got the answer ?

Hypothesis testing has been used by analysts to make inferences about the population .These tests are done to answer business questions eg . A/B testing ( version A is better than version B etc)

I'm not an expert bt I think on a dataset we can make lot of changes statistically and train a model on the data bt using such test we can in the first look out of corrections in the data before training thus saving time and computation power. Do correct if I'm wrong, it just a null hypothesis 😅 :)

It is decided before itself and yes it may changes...this value is decided by domain expertise

Look up Statistical Hypothesis testing(Inferential Stats), It depends on the confidence level (alpha) for the test. In stats this measure of likelihood is taken as .05 (5%), .01(1%), .001(.1%) or in simpler terms what is the likelihood that the alternate is true if we assume the Null to be true. The t test calculates a p value and if it is less than alpha (.05 or 5% in this case) we reject the null because the probability of getting this sample if the null is true is very small (magnitude of p value). Or in simpler terms it's not possible (or very unlikely) to get this sample by chance. If you want a better explanation please follow the Udacity Intro to inferential stats course, its free.

Usually null hypothesis is used when we say there's no difference between two groups. For example, you draw a sample from a population and want to check if there is any difference between the mean of the sample or mean of the population. You will make null hypothesis that the two means are no different. Similarly when comparing two groups if you want to check if there means are same, you will develop null hypothesis that there are no difference between two. One last example, first let's say you flip a coin 5 times and get heads 5 times. You will make a null hypothesis that my coin is no different than the normal coin. Hope this helps :)

t the end it shows -13 in power which you actaully missing . so how -13 power is greater then 0.05 even.

if you need to test effect of drug on a sample of population then same sample of population needs to be taken before and after drug dose . This is done to reduce bias. And in such type of stats modelling we use paired sample t test

Another example you can take is effect of workout on weight of a person. You need same sample of people whose weight were recorded before work out

@@i_black_hawk tanq so much

same here, @10.00 i confusing on that statement, how can 0.05 is greater than 1.139

At the end it shows -13 in power which you actaully missing . so how -13 power is greater then 0.05 even.

it was 1.12 *10^-somthing

same here, @10.00 i confusing on that statement, how can 0.05 is greater than 1.139

The t-test can be applied to any size (even n>30 also).

Specially please help us with the scikit learn library..remaining I guess has not that much importance

I have the same doubt. Can anyone explain it?

t the end it shows -13 in power which you actaully missing . so how -13 power is greater then 0.05 even.

Yes you can do but u need to ensure that null hypothesis statement is chosen in such a way that you can conduct the experiment based on that null hypothesis. For example, you observation is that you got 10 head on 10 coin toss. Now you want to check if the coin is biased or not. Now, If you take Ho(null hypothesis ): coin is biased...then the problem is how will u find the p value or conduct the experiment ..bcz the coin can be biased with any probability And suppose you take Ho : coin is unbiased ...means probability of getting 10 heads on tossing the coin 10 times is : (0.5)^10......as probability of getting one heads is 0.5 for single toss when ""coin is unbiased"" Now u will get the values as 0.00097

Since you are dealing with the sample size and not the population, the relationship you might be getting from the sample may be due to random chances. The idea behind the null hypothesis is that relationship you are observing in the variables are due to randomness. S, my null hypothesis is always of the form that, "There is no relationship between the selected variables. This is what I have derived from all the sources from the StatsLand :D . Please correct me if I an wrong.

If you execute random.choice then it will resample and change mean accordingly. For same sample , p value cannot vary.

Fix a number in random state.

Y he used np. Random. Seed (6)?

@@harishkumar-zx6vg np.random.seed(n) is used to make the random number predictable, i.e., we will get same set of numbers whenever the code executed

He was supposed to divide the p-value by 2 since its a two sided test. Two sided test occur when you use the word "difference". When it is one sided, i.e either less than or greater than, you do not divide alpha by 2

i too ...will do the same once upon a time .