This short animated video explains the concept of
"Confidence Interval" and how to construct 95% and 99%
confidence interval with help of examples
So don't go anywhere else just sit back and enjoy this video
Before we understand Confidence Interval let us first understand what do we mean by estimate ?
Everyone makes some kinds of estimates
at some point of time in their life
Suppose when are crossing the road,
you estimate the speed of the car that is approaching you
Based on estimate you decide whether to Wait, Walk or Run.
Let’s look at the estimate in the Statistical term
It gives you some facts about how the population could be
We can have two types of estimates about the population
Point estimate and Interval estimate
Lets us first look at point estimate
So point estimate is a single value or a number that is used to estimate of a population parameter
Lets take an example. Assume that this is the population and
we draw a sample out of it using any sampling technique
So we pick 4 people from this lot and we take their average
weight of these 4 samples which is coming around as 60Kg
60 Kg is a single point estimate of population mean
Point estimate is often insufficient because its either
Wrong or Right. Assuming that we know we are wrong here
and assuming that we off target by 5kg
That means whaever we have expected is a good estimate
But if we are off we are off target by 20 or 30 kg, then that is
not the right estimate
Also the point estimate is going to be different from the
population parameter due to some samling error
There is no way to know who close it is to the actual parameter
For this reason, statisticians like to give an interval estimate
which gives them some range of values used to estimate the parameter
Sample mean is a point estimate of Population mean
Now let us understand the Interval Estimates
Interval Estimate is a Range of values used to estimate an unknown population parameters.
It gives you the estimates that what is the range of values
by calculating two values or intervals UCL and LCL within which
a parameter is expected to lie.
Interval estimates of population parameters are
called confidence intervals.
And statisticians prefer interval estimates because
interval estimates are accompanied by a statement concerning
the degree of confidence that the interval
contains the population parameter being estimated
Confidence interval which is the most common type of interval
estimate is made of two elements
One is the Point estimate and other is Margin of Error
Point estimate +/- Margin of Error
It indicates the error in two ways one by the extent of its range
and other by probability of true population parameter lying
within the range
Confidence intervals are often confusing
When we construct a confidence interval we are estimating a
population parameter based on the data from a sample
So 95% confidence interval is a range of values that you can be
95% certain or confident contains the true mean of the population
The correct interpretation of a 95% confidence interval is that
"We are 95% confident that the population parameter is between 50 and 70."
Lets us understnd this with help of an example
Example 1 : Confidence Interval for Single population Mean
Example 2 : Confidence Interval for Single population proportion
So we are 99 % confident that true proportion of billing
statement that contains errors would li between 7.7 % and 12.7%