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Sampling: Simple Random, Convenience, systematic, cluster, stratified – Statistics Help

Sampling: Simple Random, Convenience, systematic, cluster, stratified – Statistics Help


To find things out about a population of
interest, it is common practice to take a sample. A
sample is a selection of objects and observations taken from the population of interest. For
example, a population might be all apples in an
orchard at a given time. We wish to know how big the apples are. We can’t measure all of them, so we take a sample of some of them and measure them. The method chosen for taking the sample depends on the nature of the population, and the resources available in terms of
time and money. The ideal is for each object in a
population to be equally likely to be chosen as part of the sample. This is called an unbiased sample. It
is also desirable for the sample to be representative of the population. if the population of apples were two
thirds red and one-third green, the sample should be similarly split.
Note that no matter what we do, there will always be sampling error or variation due to sampling, as we are
looking at a part of the population, not the whole population. The video on
variation covers these concepts more thoroughly. This video presents five methods of
sampling: For each method we will outline the process and the advantages and disadvantages. Simple random sampling is theoretically
the ideal method of Sampling. You list each member the population and
use random numbers to decide which objects are in the sample. Each object is
equally likely to be selected. This produces an unbiased sample
which we hope is representative. However it can be difficult and
expensive to take a simple random sample when
dealing with people. Simple random sampling is more practical when the
population is geographically concentrated and when a good sampling frame exists. A
sampling frame is a list of all the people or objects in the population interest. Simple
random sampling can be more easily implemented for natural and manufacturing populations. Convenience sampling is just that:
Convenient! You ask people nearby, or people who
walk past at a shopping mall, or you take the next 20 objects off the
production line. You do what is easy or convenient.
Convenience samples are often biased in some way. But, for a quick and cheap poll it may
not really matter. Convenience samples can also have
self-selection bias when people choose to participate, because they have an
interest in the issue in question. With systematic sampling you choose a starting point at random, and then systematically take objects at
a certain number apart. For example, if there are a thousand in
the population and you want a sample of fifty, you would take every twentieth object. Systematic samples are easier to
administer than simple random samples and are usually a good approximation of a
random sample. However, if there’s a pattern in the
population, certain types of objects could be chosen
more or less often than others. Cluster sampling the
population is divided into clusters which are then chosen at random. For example, departments of a business
can be clusters, or suburbs within a city. Within each
cluster, all of the objects are included in the
sample. Cluster sampling can be more convenient and practical than simple random sampling. However, if
the clusters are different from each other with regard to the elements we are
measuring, it can lead to bias or non-representativeness. Stratified sampling
seems like cluster sampling, but the strata, or groups, are chosen
specifically to represent different characteristics within the population, such as ethnicity, location, age or occupation. Within each group, a random sample is taken, sometimes in
proportion to the size of the group. Stratified sampling can lead to a very
good random representative sample. However it can be complex to administer,
and a sampling frame with considerable information about the population is
required. There are other sampling methods. The five
explained here give an idea of the advantages and disadvantages of various methods. You should attempt to use a sampling
method that produces the best result for the resources you have available. If your sample has known bias, this should
be taken into account in analysis and reporting.

100 thoughts on “Sampling: Simple Random, Convenience, systematic, cluster, stratified – Statistics Help

  1. All the sampling methods listed, except for convenience are random. A simple random sample is one form of random sample, and is arguably the ideal, as each observation is chosen completely independently of all the others.

  2.   thanks very much 

    i would like to know how to solve such types of problem?

     The following absorbance data at wavelength 543 nm were obtained for a series of
    standard solutions containing nitrite (NO2
    ) using a colorimetric method: A ¼0 (blank),
    A ¼0.220  (5 mM),  A ¼0.41  (10 mM),  A ¼0.59  (15 mM),  A ¼0.80  (20 mM).  Using
    Excel (a) to plot the calibration curve, and (b) to determine the calibration equation
    and the regression coefficient (R
    2
    ).

  3. The blonde character looks like Mello from Death Note XD he even likes chocolate. I Think teach is getting her anime on ^ ^
    Thanks for the vid it helped big time almost ready for the exam.

  4. explanations are amazing to the point and the videos are v good. One of the best stats training videos in Youtube

  5. I was given the task by the dean of my university to find out whether there could be a natural tendency of abusive behavior towards women OR/AND men, among students between 18~25 years from all different ethnicities and nationalities on our campus. I have data of N = 25000 and a n ≥ 11000. Now I have to decide on a sampling method. Should I rather go for simple random, stratified based on nationality/ethnicity, or cluster based on course? PLEASE HELP.

  6. please someone can help me how to decide the number of water samples for a specific sq km of area. Suggest me any statistical approach

  7. Probability Sampling
    -Simple Random Sampling
    -Systematic Sampling
    -Cluster Sampling
    -Stratified Sampling
    -Multi-stage Sampling
    Is this correct or not?

  8. These are just design ideas of data collecting procedures with the true intent of making the data as equi-likely as possible. But there is no guarantee of the reliability of such procedures.

  9. Well done. Collecting a sample with high quality data might require a hard work. Another challenge is how to compute probabilities once we have the data. A common  approach is to make inferences of the population mean, which is not so complicated because it allows to benefit from Central Limit Theorem. Another approach is to make inference about the probability of having a single event. These approaches answer to different questions, but both relevant. For the second, one example is seen in https://dunamath.com/homeUPC.aspx

  10. I mean upload statistics videos lecturers courses from graduate and postgraduate level including research methodology course.

  11. hi, i am a researcher currently ,i want to calculate by using cluster sampling formula to determine sample size by using s.e.x.=pqd/D D=1+(b-1)rho.my cluster no.152 and total animal population 2015,so please give me responses as soon as possible.thank you

  12. Pictures and examples help a lot in understanding. Going through your videos its simply beautiful… U are very brilliant in your teaching..

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