What is statistical error why does it differ from the mistake
- What is statistical error why does it differ from mistake explain the difference between absolute and relative error?
- What is a different step of data presentation explain it in detail?
- What are the principal criteria for a satisfactory average state giving reason to the circumstances in which it would be preferable to use mean the mode the median the geometric mean the harmonic mean?
Answer:
The difference between the actual value of the figure and its estimated value is called statistical error in the words of Professor Connor in a statistical sense “error means and the difference between the approximate value and true for ideal value accurate determination of which is not possible”
Example:
if a factory or office contains 125 employees but in round figure, it is written in 134 then the difference is called statistical error.
There is a big difference between statistical error and mistakes:
- The difference between the real value and estimated value or we can say the difference between the real value and true value created error but the wrong statistical method used in research or we calculated in a wrong way that is called a mistake in stats
- The error occurs at the time of collecting data analyzing in or at the time of interpretations but mistakes can be done at any level of research
- We cannot stop errors because it is almost impossible but we can stop making mistakes in research by using the correct method.
Difference between Absolute & Relative Error:
Absolute Error | Relative Error |
The Numerical difference between measure value and actual value called Absolute Error
Formula: Absolute Error =Measured value –actual value |
When an absolute error is divided by the true or actual value called Relative Error
Formula: Relative Error=Absolute Error/Actual value |
What is a different step of data presentation explain it in detail?
An easy way to find that it is a collection of raw facts and figures. This is the following step to present data
- Collect the correct data
- Present the data in such a way that it can attract The Reader mind at the first sight
- That I should be concise but it contains all the possible details
- After the data is organized the next steps to put the data in an attractive form
- There Is 3 basic way to present the data in statistics
- Textual method
- tabulation
- chart and diagrams
Tabulation:
As its name defines its meaning. the Symmetric arrangement of the data in rows and columns means the presentation of data in a table is called the tabulations table containing row and column each row and column creating a cell. The statistical table contains at least four parts
- Title
- Stub
- Box head
- Body of table
The main purpose of tabulation in statistics is to present the data in an effective way so that the reader can read it easily understand it quickly
Graphs and diagrams:
Diagrammatic and graphical representation is a pictorial representation of data
- graphics are useful for checking assumptions made about the data
- Graphs often suggest the form of the statistical analysis to be carried out
- the graphic gives a visual representation of the data which are easily understandable
- graphics represent the drawing of unknown interesting data in form of an attractive picture
What are the principal criteria for a satisfactory average state giving reason to the circumstances in which it would be preferable to use mean the mode the median the geometric mean the harmonic mean?
We define average as a single value that represents the whole set of data
The principal criteria for a satisfactory average are as follows
- it should be easy to calculate and simple understand
- it should be clearly defined by mathematical formulas
- it should not be affected by extreme values
- it should be based on all the observations
- it should be capable of the mathematical treatment
- it should have simple stability
The Mean:
Arithmetic means are some of the values divided by the number of the value
- it is well define
- it is easy to calculate and simple to understand
- it is based on all the observation
- it has sampling stability
- It is capable of further mathematical treatment
But there are some disadvantages of arithmetic means
- Is generally affected by Extreme values
- It is not suitable for open-end classes
- It is not suitable for qualitative data
- it is not suitable for highly secured distribution
The Median:
The median and average find the middle number in a given sequence of the number
- it is suitable for qualitative data
- it is suitable for skewed distribution
- it is suitable for open classes
But it also has some disadvantages
- it is not well defined
- it is not capable of the method mathematical treatments
- it is not based on all observation
The mode:
It is described as a more common number in the data set
The circumstances in which we use the mode
- is it is very quick to find
- it can be formed even the data is qualitative
- it is not affected by Extreme values means we can use extreme values
There are some demerits of mode
- It is not well defined it may not exist in many classes
- it is suitable to measure in the case of a small sample
- it is not unique
The geometric mean:
- It is not suitable for qualitative data
- it is not suitable for open classes
- it becomes zero if any observation 0
- it is no it is easy to calculate
- it is not simple to understand
- it becomes imaginary if any value in the data is negative
The Harmonic mean:
- it cannot be calculated if any of the observation is zero
- it gives less weight to a large value and more weight to a small value
- it is not suitable for qualitative data
- it is not suitable proper in classes