Sampling 03: Stratified Random Sampling

[@paolototaro650]

Great video! I think it's important for the viewers of this video to mention the idea of proportional allocation, which we need in stratified random sampling. Basically in this example when doing stratified random sampling as you can see the proportion of men in the population 40/100 is the same as the one in the sample 4/10 (both results give 0.4) ; same holds for the women which are 60/100 in the population and 6/10 in the stratified sample. If we have the population proportion and we want to know how many men we will need in the sample we use the formula ni = (Ni/N)*n where N is the populaion size, n is the sample size and i is the strata (ex: men). So in our example if we want to get the number of men for our sample we will compute ni = (60/100)*10, which will be indeed equal to 6.

[@nokia8707]

great video very clearly explained the concept in less than 5 minutes. Rahul you're a legend.

[@doriancorrea2554]

You just saved my quarter 2 grade in my AP Statistics class. Thank you so much!

[@skateata1]

the different colors help so much

[@iLoveTurtlesHaha]

hum, it's so easy when you explain it so well. :)

[@hangnganguyen268]

This is so helpful. Thank you!

[@dyirbeeek]

Thank you this video! I watched several videos but this one is the best!

[@陈思哲-p8m]

truth man

[@spongebobby188]

So clearly explained! Great job 👍

[@arihoeliab7831]

Good work!

[@91dikon]

this is wonderful. thank you!

[@leah1863]

Good example. Very clear.

[@drumilmodi9778]

Thanks man great video on the topic.

[@wolfyloves2play452]

How do present data in stratified sampling

[@trigo973]

I love your penmanship wowww

[@Moon-rq5vb]

Why do the book have a different strata? Damn I am stressed ToT.... I need to rush everything...

[@mohamedmahmoudgharbya4509]

you are great ♥

[@NurAthirah-n1x]

how do u get the number of people who do /not smoke?

[@gary123622]

stratify the groups so group them men and women for example and you divide them into 2 groups and these groups are called strata so 40 women you choose 4 women and 60 men so 6 men and this is a simple random sample and then you will have 3 different samples which are always 4 women and 6 men so from the strata you choose a simple random sample and then you bring both simple random samples together to make a stratified random sample

[@bathtoaster335]

heard of a fullstop?

[@mohamedeesakadwa1091]

How do you calculate the number of strata?

[@aathiramarayil]

This is easy to understand. Thank you

[@elijahd.spragueph.d8905]

Very helpful, thank you for making this video.

[@likeakhyar3920]

kzbin.info/www/bejne/aGGkaJqhgNVrq8Usi=H4AX7voGVRlNt_T3

[@nicolezink8261]

Super helpful, thanks for sharing 🙏

[@rizmandesu]

thank you, the explanation is very clear

[@jpoysvlog8962]

This is what i want, there's have a drawing to formulate the sample.

[@prapsdy]

So how did you decide on the ratio of 6:4?

[@alienfromouterspace5425]

hello, so i am not sure myself, i actually had the same question as you and then i thought maybe it was like this , like we have 40 women and 60 men so the whole population is 100 and we need 10 out of these so out of 40---->x and 100----->10 so if we (40*10)/100 we'd get (x= 4) , same for 60----->x, 100---->10 so if we (multiply 60*10)/divide it by 100 we'd get (x=6) , i hope this is correct

[@welovemusic8542]

Randomly?

[@jonathanbower863]

So good

[@thiduyenbui9261]

Could anyone help me to answer this question please? . How does stratified method generate simple random samples? I get a bit confused at the "Stratified sample" method It doesnt sound that we are making random samples because if we break the population into smaller groups and pick randomly, for example we have 5 levels of education and each level is one subgroup, then we prioritize to have samples which include all levels of education. It means that you miss out samples which maybe only contains 4 or 3 levels of education. In the beginning, we have a very clear definition for " a simple random sample is EVERY SET OF n individual has an equal chance of being sampled", in the stratified sample we dont let every set has an equal chance of being sampled.

[@ricardog4459]

my understanding is that the attribute that you are interested in studying and the attribute to stratify are different. Say that you want to study the distribution of height, then you stratify education and study height as a random variable. At least that's how I am applying stratification, I am not so interested in that attribute but in another one, but I cannot study the whole data, so I take a stratified sample to keep it representative of the population

[@movies_reload]

@@ricardog4459 I have a doubt can you help me. pls. Does stratum means the sample selected from the strata.🙄 Pls help me

[@ricardog4459]

@@movies_reload Yes. That's my understanding. You stratify the data on, say, property 1, and randomly select from each strata of property one, data with the property 2. But I would say better ask your teacher at school, he should be able to give you a better explanation and an example. cheers

[@AawhanKafle]

we need to classify the population based on specific class and find the constituting ratio, then for sample units we select based on population ratio and sample size. for question @thiduyen asked, lets suppose there are 1000 students in our population and their are five level of education and 200 in each different level of education then we first need to stratify levels and no of students i.e level 1 200 , level 2 200 , level 3 200, level 4 200 and level five 200, this gives us the ratio 200:200:200:200:200(that is 1:1:1:1:1). that means our sample should have same number of sample units from each strata. let us say we have sample size of 25 then 5 students is selected from level 1 randomly, it is done by randomly sampling five students from strata of 200 level 1 students, similarly 5 students is selected from level 2 by randomly sampling five students from strata of 200 level 2 students and so on. here the sample ratio is 5:5:5:5:5 (that is 1:1:1:1:1) so it is a better representation of the population.

[@AnilGrover-i1k]

Love this little cigs 😂

[@NocheFluorescente]

Thank you ! Very Clear !

[@ttremms]

Really helpful! thanks!

[@PriyaKumari-xs1vr]

Thank u so much sir .

[@its.the_none]

Arigathanks gosaimuch for this?

[@ness_cek]

this is so easy to understand, thanks heaps!

[@movies_reload]

I have a doubt can you help me. pls. Does stratum means the sample selected from the strata.🙄 Pls help me

[@khaibaromari8178]

@@movies_reload I hope you've already figured the answer out by the time you read this reply. Well strata is the plural form of stratum which is obviously is a noun. Multiple stratum is called strata. In field of sampling stratum means a layer (to be more spesific a spesification) that you can divide your population based on. It could be sth like gender, age groups, race, country of origin, and so on. To have higher rate of representativity in your sample you may use stratification to draw your sample randomly from each of your stratum.

[@ndizeyeimananiyonkuru233]

Thanks alot my facilitator

[@kevinkamba-comestay3706]

Amazing ! thanks

[@funnydikhao1179]

It may be understood in bilingual mode or in Hindi

[@jacks3606]

Thank you!

[@45zharelmarpha]

Thank you. :-)

[@waseemzaman4300]

this is not stratified random sampling but quota sampling. correct me if am wrong

[@suriyanshankar2017]

Thank you so much

[@farzananaved7857]

Thanks a lot.

[@aymansaid1253]

Thank

[@baindlavamshikrishna5556]

This one was Better way of understandable

[@movies_reload]

I have a doubt can you help me. pls. Does stratum means the sample selected from the strata.🙄 Pls help me

[@giovannithegreat8119]

@@movies_reload Stratum Is the plural form of strata

[@libraryofexpression]

mrs hannah

[@Superman-ob3nj]

Thank you

[@bottlebrush4824]

Is it just me or is most of the people in the comments section, Indian?

[@shinellmontgomery3793]

Still don't get it

[@jpoysvlog8962]

This is what i want, there's have a drawing to formulate the sample.

[@isabelginazodwaogwal9546]

Thank you!

[@edithmambiri7089]

Thank you