Descriptive statistics & NPC ( Normal Probability Curve)

 

Descriptive statistics & NPC ( Normal Probability Curve) 

Descriptive statistics is one of the two main branches of statistics.

Descriptive statistics provide a concise summary of data. We can summarize data numerically or graphically. For example, the manager of a fast food restaurant tracks the wait times for customers during the lunch hour for a week. Then, the manager summarizes the data.

Numeric descriptive statistics

The Researcher calculates the   numeric descriptive statistics:

Graphical descriptive statistics

The Researcher examines the graphs to visualize the wait times.

Normal Probability Curve

Normal: It means Average

Probability: It is nothing but a chance of appearing

Curve: A line or outline which gradually deviates from being straight for some or all of its length.

 

Normal Probability curve is drawn to show the equal distribution of scores in the either side of the mean with a perfect bell shaped curve without touching the base line.so the right side of the centre is a mirror image of the left side.  That is called symmetric. The area under the normal distribution curve represents probability and the total area under the curve sums to one. It is also known as called Gaussian distribution, after the German mathematician Carl Gauss who first described it.

In a normal classroom, we always observe that, most of the students get average marks, very few get excellent marks and very few get poor marks. So if we draw graph or curve of such data we get Normal Probability Curve.

Example: -Many human characteristics like height, weight, strength, learning ability, cooperativeness, social dominance etc.

Application:

1.    To Evaluate student’s performance from their score

2.    To compare two or more distribution terms in of overlapping

3.    To calculate the percentile rank scores in a normal probability distribution.

4.     To normalize a frequency distribution, an important process in standardizing a psychological test or inventory.

5.    To test the significance of observed measures. To find out sampling errors.

6.    To determine the percentage of cases within the given limits or scores.

7.    To know how many students fall below and above the average performance.

8.    It gives the limits of the scores.

9.    To find out the relative difficulty of test items.

10. To find out the number of cases between mean and one standard deviation.

11. To divide a group according to same ability and assigning same grade like A- VERY GOOD B- GOOD C-AVERAGE D-POOR E- VERY POOR

12. To find out the percentage rank of a student from the scores and score from the percentile rank