The Two-Point Threshold sample essay

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The Two-Point Threshold sample essay

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I. Introduction
In the two-point threshold experiment it is obtained how close two distinct sharp point can be together for a person to feel two points instead of one. The value of the two-point threshold is the minimal distance at which the subject can feel two distinct points. The principle behind this experiment is the fact that mechanoreceptors are not distributed homogeneously in the skin of the human body. There are areas with a higher density and areas with a lower density of mechanoreceptors, making this certain area more or less sensitive. However, the smaller the distance is where the subject can feel two distinct points, the more mechanoreceptors must be present in this area, enabling a person to feel small details in that part of the body. In this experiment we will test the two-point threshold at five ares of the body: the back of the hand, the palm of the hand, fingertip, the back of the neck, and the calf of the leg. With these given areas, the fingertips will have a smaller two-point threshold than the back of the hand.

II. Materials and Methods
For this experiment a compass is used to apply two sharp points to the skin at the same time, and a ruler to measure the distance of these points. The independent variable for the experiment is the are where the two-point threshold is measured. The dependent variable is the two-point threshold, or in other words the smallest distance at which the subject can distinguish between one and two points at one of the five tested areas. At first the compass is set on the smallest value, 2mm, and applied to a certain area. If the subject does not feel two distinct points the distance between the points will be increased until the subject can feel two points. That way the smallest distance, the two-point threshold is obtained.

III. Results

The Two-Point Threshold Values For All Subjects
Calculations/ T-Test

First the average two-point threshold is calculated for both areas by summing up all values and dividing the sum by the number of values,7. For the back of the hand it is an average of 22.4mm, and for the fingertips 4.71mm. Then the difference of each value to the average value is calculated and inserted in to the formula to calculate the standard deviation, where n is the number of subjects/values.

X-∅X (back of hand)
⇒ square
X-∅X (fingertips)
⇒ square
4,4
19,36
2,71
7,34
4,4
19,36
0,71
0,5
7,4
54,76
5,3
28,1
16,6
275,56
0,3
0,1
7,6
57,76
1,7
2,89
6,4
40,96
1,7
2,89
1,4
1,96
1,3
1,69
The squares for both independent variables will now be summed up and divided by n-1= 6 before the square root is taken.

Back of the hand:

Fingertips:

To calculate the T-value the difference of the average values is subtracted by the square root of the sum of the two SD square divided by the number of subjects, 7.

The calculated T-value for this experiment is 9,46.

The Average Two-Point Threshold and Standard Deviation for the Fingertips and the Back of the Hand
The graph shows clearly that the fingertips have a much smaller two-point threshold with an average of 4.71mm, than the back of the hand with 22.4mm. It also shows that the standard deviation for the fingertips is much lower with ±2.69mm than the standard deviation of the subjects at the back of the hand with ±8.85mm.

IV. Conclusion
The results of the experiment support the hypothesis that the fingertips have a smaller two-point threshold than the back of the hand. It is supported by the average two-point threshold of both areas, while the fingertips have and average of 4.71mm and the back of the hand shows and average result of 22.4mm as two-point threshold which is almost five times greater compared to the average fingertip value. The T-test is a statistical hypothesis test to see if the hypothesis is supported. In this experiment a T-value of 9.46 was calculated to 6 degrees of freedom. According to the table there is a 0.0001% chance that the hypothesis is incorrect. So in other words this T-value supports the hypothesis with over 99%.

A source of error is certainly the number of subjects in the experiment. For a strongly supported hypothesis I would suggest a follow up experiment with many more subjects to make sure this hypothesis is still supported because only a few too high or low numbers can certainly change the results with only seven subjects. Also I would suggest to have only one tester in the follow up experiment because in this experiment there were seven testers, one for each subject and everybody measures slightly different. So instead of having human error involved from one tester, we have errors involved from seven. The last source of error is within the calculations. Rounding errors here and there can make a difference if the results are close together.


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