Tutorial 2- What is Population And Sample And Sampling Techniques In Hindi?

A population is the entire group that you want to draw conclusions about. A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.
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[jazzad]

Exit pole- random sampling
Disease information- convenience sampling
Household expenses- stratified sampling

[NatureSymphonyHQ]

Thank you for your amazing content!
Your explanations make complex topics easy to understand. Just wanted to clarify one point—what you described as Convenience Sampling actually matches Purposive Sampling because it involves selecting participants based on a specific interest (like surveying only Data Science enthusiasts for a study on Data Science).

Convenience Sampling, on the other hand, is when researchers collect data from people who are easily available, even if they are not the most relevant to the study (like asking random friends or family members about Data Science, even if they have no interest in it).

Your videos are really helpful.
😊

[usmanayaz]

1. Exit Poll: Simple random sampling
2. Disease Information: Startified sampling
3. House hold expenses: convenience sampling or voluntary response sampling

[nocturneechoes]

Additional types of sampling

Cluster Sampling:
Cluster sampling is like picking groups or clusters of individuals from a larger population instead of selecting individuals one by one. Imagine you have a big box of candies, and instead of picking candies individually, you divide the candies into small bags (clusters) first. Then, you randomly choose a few bags, and all the candies in the selected bags become your sample. It's an efficient way to sample when it's hard or expensive to reach individuals directly.

2.Judgmental Sampling (Purposive Sampling):
Judgmental sampling is when you choose specific individuals or groups deliberately based on your judgment or knowledge of the population. For example, if you want to study the opinions of experts in a field, you might handpick experts you know and include them in your sample. This method can be useful when you need specialized insights, but it may introduce bias if your judgment is subjective.

3. Snowball Sampling:
Snowball sampling is a method used when it's challenging to identify or reach individuals directly. It's like starting with a small snowball and rolling it downhill, and as it rolls, it picks up more snow and becomes bigger. Similarly, you begin by finding a few initial participants (who are part of the target group), and then you ask them to refer others who fit the criteria. This process continues, and your sample grows like a snowball. It's often used in studies involving hard-to-reach or hidden populations.

[vaibhavbisht]

Following Your Stats Playlist, Really Loved it .i Hope you upload all videos soon so that I can learn from them.❤

[AshishRai-ekqu]

Great Explanation Sir. I am already a student of I neuron FSDA 2.0.

[shakuntalakumari]

Thank you so much for the free provided valuable content..best than the paid version.

[gungunmishra]

1.Exit poll: simple random sampling.
2.Disease information: convenience sampling.
3. house hold expanses: stratified sampling.

[amitjaisawal]

sir aap best ho par apki writing 🤦‍♂🤦‍♂ but you are best 👍👍💕💕

[amit_tiger]

Sir ap no doubt bahut achha padhate hai aur bahut achhe se samajh me bhi ata hai lekin thoda Chhota likhte hai ap to dubara dekhne me ham padh nhi pate hai isliye please thoda bade Akshar me likhiye 🙏🙏

[_tyit_vivek_purohit]

1) Exit Poll - Random Sampling
2) Disease Information - Voluntary Response Sampling
3) Household expenditures - Stratified Sampling if Women or Voluntary Response Sampling if the situation requires the conclusion from only married people....
please correct me if am wrong and your valuable feedback would strengthen my knowledge, thanks for the wonderful video.

[Coool_Pankaj]

Sir, please tell secret of your energy and dedication 😇 thanks for everything.. 😇

[ayushtripathi]

Generally we use simple random sampling when we have homogeneous type of data. When we have heterogeneous type of data then we divide these data into varipus statras and then select observations from strats randomly ..then this procedure is called stratified random sampling.

[saifalimuhammadi]

thank u for help, this is great class for aspirent of iitm data science students

[Karanchadha]

Sir i this is really helpful and I am binge-watching your tutorial..
Sir i want to be a data analyst and along with this stats series . What are others skills to learn which I can learn free of cost?

[ayushtripathi]

In stratified random sampling our objective is to make strats homogeneous as much possible then we simply use simple random sampling.

[vlogplanet]

WHAT IS STATISTICS?

Definition of Statistics, Population, sample Descriptive and inferential Statistics, Observations, Data, Discrete and continuous variables, Errors of measurement, Significant digits, Rounding of a Number, Collection of primary and secondary data, Sources, Editing of Data. Exercises.

PRESENTATION OF DATA:

Introduction, basic principles of classification and Tabulation, Constructing of a frequency distribution, Relative and Cumulative frequency distribution, Diagrams, Graphs and their Construction, Bar charts, Pie chart, Histogram, Frequency polygon and Frequency curve, Cumulative Frequency Polygon or Ogive, Historigram, Ogive for Discrete Variable. Types of frequency curves. Exercises.

MEASURES OF CENTRAL TENDENCY:

Introduction, Different types of Averages, Quantiles, The Mode, Empirical Relation between Mean, Median and mode, Relative Merits and Demerits of various Averages. properties of Good Average, Box and Whisker Plot, Stem and Leaf Display, definition of outliers and their detection. Exercises.

MEASURES OF DISPERSION:

Introduction, Absolute and relative measures, Range, The semi-Inter-quartile Range, The Mean Deviation, The Variance and standard deviation, Change of origin and scale, Interpretation of the standard Deviation, Coefficient of variation, Properties of variance and standard Deviation, Standardized variables, Moments and Moments ratios. Exercises.

PROBABILITY AND PROBABILITY DISTRIBUTIONS.

Discrete and continuous distributions: Binomial, Poisson and Normal Distribution.

SAMPLING AND SAMPLING DISTRIBUTIONS:

Introduction, sample design and sampling frame, bias, sampling and non sampling errors, sampling with and without replacement, probability and non- probability sampling, Sampling distributions for single mean and proportion, Difference of means and proportions. Exercises.

Sir ye topic cover krwa Dy

[AbhishekRajput-bcux]

Exit poll- simple random sampling.
Disease information- convenience sampling.
household expenses-stratified sampling.

[amitjaiswal]

Thank you sir for a such a amazing video can you please make video on how should fresher should switch company in hindi.

[sadaf]

Can you further explain in more detail the words" Equal Chance" in the random sampling and "Non-Overlapping" in the stratified sampling.