Using Calipers

There are two Calipers introduced to us during the activity-vernier and micrometer. I already mentioned these two together with the thai grain rice experiment before in my first post. The difference here is that I'll explain them in more detail and let you see how they are instrumental in measuring the length of the grain rice. In other words, this post will be more about our data sheets and analysis during the experiment.

In measuring the length of a thai grain rice, we prefer to use the vernier caliper because it's more convenient. Below are the data:

Bin Min	Bin Max	Bin Mid	Count	Frequency	Normalized	Gaussian Fit
5.0000	5.0692	0.0346	1	0.0932314003	1.18441554618857E-045	0.0130280707
5.0692	5.1384		0	0	7.40964530346269E-047	0.0237251447
5.1384	5.2076		0	0	7.40964530346269E-047	0.0423403352
5.2076	5.2768		0	0	7.40964530346269E-047	0.0740485178
5.2768	5.3460		0	0	7.40964530346269E-047	0.1269097838
5.3460	5.4152		0	0	7.40964530346269E-047	0.2131525319
5.4152	5.4844		1	0.0932314003	1.1844155461886E-045	0.3508347019
5.4844	5.5536		0	0	7.40964530346269E-047	0.5658889956
5.5536	5.6228		0	0	7.40964530346269E-047	0.8944923075
5.6228	5.6920		0	0	7.40964530346269E-047	1.3856024039
5.6920	5.7612		0	0	7.40964530346269E-047	2.1033778824
5.7612	5.8304		0	0	7.40964530346269E-047	3.1290509464
5.8304	5.8996		0	0	7.40964530346269E-047	4.5616779613
5.8996	5.9688		0	0	7.40964530346269E-047	6.5170832243
5.9688	6.0380		0	0	7.40964530346269E-047	9.1242792493
6.0380	6.1072		2	0.1864628007	1.82502032049988E-044	12.5187373118
6.1072	6.1764		3	0.279694201	2.71074231271194E-043	16.8321324991
6.1764	6.2456		2	0.1864628007	1.82502032049988E-044	22.178614514
6.2456	6.3148		2	0.1864628007	1.82502032049988E-044	28.6382410689
6.3148	6.3840		0	0	7.40964530346269E-047	36.2388932109
6.3840	6.4532		4	0.3729256013	3.88119703027619E-042	44.9386649929
6.4532	6.5224		5	0.4661570017	5.35673387670102E-041	54.6112433742
6.5224	6.5916		1	0.0932314003	1.1844155461886E-045	65.0370178378
6.5916	6.6608		6	0.559388402	7.12674757987528E-040	75.9024563134
6.6608	6.7300		5	0.4661570017	5.35673387670102E-041	86.809588688
6.7300	6.7992		3	0.279694201	2.71074231271194E-043	97.2962771429
6.7992	6.8684		6	0.559388402	7.12674757987528E-040	106.8664553253
6.8684	6.9376		8	0.7458512027	1.12991237199024E-037	115.0279135589
6.9376	7.0068		11	1.0255454037	1.71278078871405E-034	121.3337825975
7.0068	7.0760		13	1.2120082044	1.88115430804898E-032	125.4229144574
7.0760	7.1452		9	0.839082603	1.34650151135402E-036	127.0541035215
7.1452	7.2144		6	0.559388402	7.12674757987528E-040	126.1296413469
7.2144	7.2836		4	0.3729256013	3.88119703027619E-042	122.7050062954
7.2836	7.3528		13	1.2120082044	1.88115430804898E-032	116.9833516354
7.3528	7.4220		18	1.678165206	1.25091329221721E-027	109.2955536904
7.4220	7.4912		9	0.839082603	1.34650151135402E-036	100.06854514
7.4912	7.5604		4	0.3729256013	3.88119703027619E-042	89.7861467167
7.5604	7.6296		4	0.3729256013	3.88119703027619E-042	78.9473829867
7.6296	7.6988		3	0.279694201	2.71074231271194E-043	68.0272319038
7.6988	7.7680		1	0.0932314003	1.1844155461886E-045	57.4439793377
7.7680	7.8372		6	0.559388402	7.12674757987528E-040	47.5360281728
7.8372	7.9064		2	0.1864628007	1.82502032049988E-044	38.5494266427
7.9064	7.9756		0	0	7.40964530346269E-047	30.6358271756
7.9756	8.0448		2	0.1864628007	1.82502032049988E-044	23.8593141469
8.0448	8.1140		0	0	7.40964530346269E-047	18.209706944
8.1140	8.1832		0	0	7.40964530346269E-047	13.6196082049
8.1832	8.2524		0	0	7.40964530346269E-047	9.982582979
8.2524	8.3216		0	0	7.40964530346269E-047	7.1703094678
8.3216	8.3908		0	0	7.40964530346269E-047	5.0471885349
8.3908	8.4600		0	0	7.40964530346269E-047	3.4815914916

(C.G. Sevilla, What is the Length of a Thai Grain Rice?, blogpost from http://thephysics101p1files.blogspot.com/2015/02/what-is-length-of-1-rice-grain.html , accessed: 02/16/2015)

*data gathered by Christopher Gerard Sevilla, Jessa Kara Gascon, and Joevanie Cañete Jr. during their Physics 101.1

And its graph as expected will be bell-shaped

(C.G. Sevilla, What is the Length of a Thai Grain Rice?, blogpost from http://thephysics101p1files.blogspot.com/2015/02/what-is-length-of-1-rice-grain.html , accessed: 02/16/2015)

At a later date we we're given the class data. In comparison, the number of data of the class is relatively much greater than just our class data. What does this mean? We expect a "better" curve so a better approximation of the length of a thai grain rice.

(C.G. Sevilla, What is the Length of a Thai Grain Rice?, blogpost from http://thephysics101p1files.blogspot.com/2015/02/what-is-length-of-1-rice-grain.html , accessed: 03/16/2015)

On Measurements

This article aims to fill the insufficiency of last report in Measurements.

As what we have said in the last post, Measurements are inherently "uncertain". That is because measurements in contrast to numbers are not definite. There are three why's that contribute to this uncertainty. First is the "nature of quantity being measured". There are quantities whose measurements fluctuates depending on some factors like temperature. Second, the judgment of the experimenter. Is it 2 or 2.5? 2.5 or 2.75? It depends on the experimenter (the rightness of his judgment). And lastly, the limitation of the measuring device. There are some devices calibrated only to some extent while there are others that are accurate even up to tenths of decimal places. In every measurement there is always uncertainty. A good measurement is one which has an error report. An error or difference from the accepted value is called systematic error. This one has problems with accuracy. It might be because of problems within the system (wrong measurement procedure, biasness, and the likes). The second error is called random error. When you have this error, you lack precision. You may be accurate but your data is not close to each other. Therefore a good error count is one that considers systematic and random errors- one that considers both accuracy and precision.

On this part I will talk about the different Levels of Orders of Approximation. What are these? Basically, they lessen uncertainty in measurements. The first one is Order of Approximation. Quite straightforward. You just aproximate the measured value. The example given by our prof is the Fermi question, which asks about the atomic bomb explosion radius. Surely you would just approximate (you won't even measure it all!). For me this is just for practicality but not really for accuracy. Second is using Significant Figures. This is about estimated uncertainty that is 1/2 the device precision or least count. The number of significant figures in the measurement is equal to the number of digits you are certain. Third is the Limited measurements, which is just the MIN-MAX. What I wanted to focus on (that our prof also focused) is about the Distribution Function. 

It says that no matter how many data you have as long as it is normalized it will just approach a bell shape (central limit theorem). The more data, the better the graph will look like.

                                      C.G.Sevilla, What is the Length of a Rice Grain?, blogpost from http://thephysics101p1files.blogspot.com/2015/02/what-is-length-of-1-rice-grain.html?view=sidebar , accessed: 02/16/2015

A Glimpse of Being a Physicist

          The first meeting was a not so ordinary encounter with physics. Well, not so ordinary for a student inexperienced in working under lab conditions.

          Let me start by describing the physics lab at NIP. It has enough space for just a few students (20 maybe) and the tables are structured in a way that members of a group face each other while sitting for a better discussion and cooperation. In short, it's just right and conducive for experimenting. The ambience was complemented by our exerienced prof, Dr. Rene Batac. I had encountered him before during Physics 10. He is great indeed.

          The lab work started with Measurements, which for me is the fundamentals of physics- quantifying things. I learned that measured values are inherently "uncertain". To combat uncertainty, we must be precise. In a 30-minute talk, there are 4 ways to lessen uncertainty, that is to be precise. A) Order of Magnitude Approximation B) Significant Figures C) Min-Max and D) Based on a distribution function. Dr. Batac said we would utilize based on a distribution function. The equation is quite complicated though but the thought was if I still remember it correctly, the graph of data with normal distribution will be a bell shape.

          The second part was using calipers. We were taught how to use a vernier and micrometer caliper. I remember the vernier caliper back in high school during our SIP. But because I really dont know how to use a vernier caliper, I just used a ruler instead to measure the minimum inhibitory concentration of a seaweed extract (kinda off-topic XD). Anyways, we were taught about the main scale and fractional scale in the vernier and the 2 sound "ticks"  in a micrometer. To make it short, we now know how to use them (maybe not so pro but good enough). As proof, we were able to send our data to Dr. Batac. The data was about the measurements of a thai grain rice. And after it was encoded in excel, the graph really showed a somehow bell shape or a near bell shape. I remember our group was able to make 50 measurements. Lesson learned: don't be biased in measurements.

          That is all folks for tonight's "pisikasayahan" (funphysics) at NIP. Watch out friday next week for our next lab experiment!

*this is in compliance for my Physics 101.1 under Dr. Rene Batac