DSA 2022

[Mr.025413Clumsy]

ainunnajib\" post_id=\"2075688\" time=\"1658472272\" user_id=\"166513:

50-50 chance according to Bayesian, or either 100% or 0% according to Frequentist

Wow.. I had to look those terms up! Clearly, I need to start at P1 level again.

[ainunnajib]

Lavanya123\" post_id=\"2075690\" time=\"1658473487\" user_id=\"143620:

A Waitlist is another possibility isnt it? We are in the same boat as you i guess, not interview mail

I would think the eventual waitlist ones should also get interview invites.
Also here in this forum someone said that historically waitlist from NUSH is 100% eventual confirmation, which makes sense considering NUSH still had SIE too (i.e. even after clearing waitlist, they still have some spots left).

[Zeal mummy]

Bayesian inference is a method of statistical inference in which Bayes’ theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law. In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability".


Frequentist inference is a type of statistical inference based in frequentist probability, which treats “probability” in equivalent terms to “frequency” and draws conclusions from sample-data by means of emphasizing the frequency or proportion of findings in the data. Frequentist-inference underlies frequentist statistics, in which the well-established methodologies of statistical hypothesis testing and confidence intervals are founded.

—-
Frequentist inferences stand in contrast to other types of statistical inferences, such as Bayesian inferences and fiducial inferences. While the "Bayesian inference" is sometimes held to include the approach to inferences leading to optimal decisions, a more restricted view is taken here for simplicity.

Bayesian inference is based in Bayesian probability, which treats “probability” as equivalent with “certainty”, and thus that the essential difference between the frequentist inference and the Bayesian inference is the same as the difference between the two interpretations of what a “probability” means. However, where appropriate, Bayesian inferences (meaning in this case an application of Bayes’ theorem) are used by those employing frequency probability.

There are two major differences in the frequentist and Bayesian approaches to inference that are not included in the above consideration of the interpretation of probability:

1. In a frequentist approach to inference, unknown parameters are often, but not always, treated as having fixed but unknown values that are not capable of being treated as random variates in any sense, and hence there is no way that probabilities can be associated with them. In contrast, a Bayesian approach to inference does allow probabilities to be associated with unknown parameters, where these probabilities can sometimes have a frequency probability interpretation as well as a Bayesian one. The Bayesian approach allows these probabilities to have an interpretation as representing the scientist’s belief that given values of the parameter are true (see Bayesian probability - Personal probabilities and objective methods for constructing priors).

2. While "probabilities" are involved in both approaches to inference, the probabilities are associated with different types of things. The result of a Bayesian approach can be a probability distribution for what is known about the parameters given the results of the experiment or study. The result of a frequentist approach is either a "true or false" conclusion from a significance test or a conclusion in the form that a given sample-derived confidence interval covers the true value: either of these conclusions has a given probability of being correct, where this probability has either a frequency probability interpretation or a pre-experiment interpretation.

[Mr.025413Clumsy]

Oh great, not only is my Maths bad, my English is rubbish too.

[floppy]

Is there an English translation?

[Zeal mummy]

I just copy and past from wiki. I didn’t write any of those… but it’s basically statistics.

[watermum]

ainunnajib\" post_id=\"2075693\" time=\"1658473628\" user_id=\"166513:

I would think the eventual waitlist ones should also get interview invites.
Also here in this forum someone said that historically waitlist from NUSH is 100% eventual confirmation, which makes sense considering NUSH still had SIE too (i.e. even after clearing waitlist, they still have some spots left).

For kids not invited for interviews, possible to be under wait list for nush? Or it’s a straightforward CO or reject?

[mom2girls]

ainunnajib\" post_id=\"2075652\" time=\"1658462080\" user_id=\"166513:

In general perfect score or near perfect for Math but AL2 for Science. Both Languages at AL3 level.

This is why DSA is our “only” chance to get good school for him 😅 too imbalanced.

Your child is surely gifted. Best wishes to him. DSA may seem like a probable or only route but there are many other schools that may help him grow beyond his potential.
There are schools that offer robotics /coding as niche subjects as well.
All the best to your son for RI and NUSH. He will definitely get a school of his liking and pave his path of success.

[nescafe]

Hi, anyone received interview email from CCHM after wushu trial last week?

[Coffeelatte03]

Hi all just wana ask as i saw someone said in the previous post that we should put in our best subject for DSA. I’m just wondering, if let’s say I get AL1 for math and AL2 for EL but there are 20 other people who’s getting AL1 for math but none for EL, should I still be putting math as my choice as it’s my best subject? Or should I use EL instead? Thanks