For this video, aalamin natin ang pinakaiba ng descriptive at inferential statistics. Pero bago tayo pumunta doon, let us first define few terms. Una ay population. Population is the entirety of the group including all the members that forms a set of data. When you talk about population, yung kabuuan ang pinag-uusapan natin.
Lahat ng members ng pinagkukuha na natin ng data, that is our population. Halimbawa, isang buong strand or isang buong school. Next is sample. Sample contains a few members of the population.
Part lang sila ng ating population. Samples were taken to represent the characteristics or traits of the population. For example, eto yung buo nating population, yung buong bilog, and then yung sample is yung mga maliliit natin na blue. From the entire population, kumukuha lang tayo ng members.
Kumukuha tayo ng... samples. Pwede rin tong part na to na eto yung buong population natin and then isang buong part yung kinukuha natin as our sample. Eh sir, bakit kailangan pa natin kumuha ng sample? Kasi kapag masyadong malaki yung population, it will take you a lot of time and resources para makakuha ng data sa lahat.
Kaya kumukuha tayo ng sample as the representative nung buong population natin. Yung characteristics or traits nung sample ... Yun na yung magiging characteristics or traits ng ating population in that specific study. Now that we have defined ano ba yung population at ano ba yung sample, punta na tayo sa ating main topic which is descriptive and inferential statistics.
So what is descriptive statistics? Descriptive statistics used to say something or describe a set of information collected. It can also be represented with graphs. Sa descriptive statistics, ang keyword natin ay describe.
Sinasabi natin kung gaano kataas o gaano kababa yung dataset natin o kaya naman gaano karami yung isang data compared dun sa kabila. And we are involved with one specific population na pinagkukuhana natin yung ating information. Ano ba yung common tools na ginagamit natin sa descriptive statistics?
They are measures of central tendency, yung kinukuha natin yung middle value na mag-describe dun sa ating dataset. Next is measure of variability. Ang pinag-uusapan naman natin dito ay yung spread or yung kalat ng ating dataset. As an example, this is a descriptive statistics in a math test. 32 out of 40 students were able to receive a passing mark.
Sa isang klase, 32 out of 40 yung nakapasa. If we will be computing it, this is 80% nung ating population ng ating klase. The average score of the class is 82 out of 100. We are just merely describing yung data na nakuha natin from our population.
That is descriptive statistics. Next is inferential statistics. Ang inferential statistics, it is used to say something about a larger group. Meron tayong mas malaki, which is our population using information collected from a small part of that population.
So gagamit tayo ng sample to say something about our population. Kasi sa descriptive statistics, ang concern lang natin isang grupo ng tao, dinidescribe natin sila, kumukuha tayo ng data about them, pero ang inferential statistics, gumagamit tayo ng sample for the population. The common tools are hypothesis testing and regression analysis. Pero for this one, gagamit muna tayo ng masimpleng example.
In a sample survey conducted, 65% of Filipino Generation Z prefer to drink milk tea than coffee, while 34% of Filipino Millennials prefer to drink milk tea than coffee. Ibig sabihin, mas mataas yung preference ng mga Gen Z, Filipino Gen Z, compared dun sa Filipino Millennials natin which is just 34% sa pag-inom ng milk tea compared to coffee. This is inferential statistics kasi hindi naman kaya nung ating survey na kumuha ng data sa lahat ng Filipino Generation Z at Filipino Millennials. Kaya kumuha sila ng sample ng mga nandito sa group na ito at sa group na ito para ma-represent yung group nila.
So again, inferential statistics, you are taking samples to represent your population. And medyo malawak ang inferential statistics. Mas marami pa tayong magagawa, mas maraming application ang area ng statistics na to. And that's it for the difference between descriptive and inferential statistics.