Suresh Sekar

4 Types of logical relationships:
1.   Finish to Start
2.  Start to Start
3.  Finish to Finish
4.  Start to Finish
 
Finish to Start: 
A logical connection in which a next-in-line cannot start until a forerunner activity has finished.
This relation constraint doesn’t impact the independent activity. It affects only the dependent, so in the below case, it is activity B.

The first character ‘F’ shows the finish state of activity A, at this state, activity B can started. The second character ‘S’ is the rule on the next step to make it dependent on the Finish state of the successor activity.
Example:
You cannot start next stage until you finish the design. In this case, development is the dependent activity on the design activity.

Start to Start:
A logical relationship in which a next-in-line activity cannot start until a forerunner activity has started.
So if activity B is leap to this relation, which means it cannot start till the forerunner (independent) activity A is started.

 
The forerunner activity B start is dependent on the start state of next-in-line activity A. The start of activity A operate the start of activity B.
Example:
The activity of marketing booklet preparation cannot start until user manual documentation has begun. In this way, after the opening of activity A, both A and B will can go in parallel.
 
Finish to Finish:
A logical relationship in which a next-in-line activity cannot finish until a forerunner activity has finished.”
So if activity B is leap with this relation, which means it cannot finish till the forerunner (independent) activity A is finished. So, B needs to finish the deliverable and parallelly keep working with A till the time A is not completed.

 
Example:
Like, the telecast of a cricket match cannot finish until the match is finished. So the match is not depended in telecast, but the telecast is. If the match will take longer than the initially planned time the telecast will also continue till that time..
 
Start to Finish:
“A logical connection in which a next-in-line activity cannot finish until a forerunner activity has started.”

So if activity B is leap to this relation, which it cannot finish till the independent activity A starts. It looks bit confusing because in some cases, forerunner activity gets performed before the next-in-line activity. But, in this case, the next-in-line is happening first. But even if the next-in-line is happening first, the forerunner is not at all restricted by next-in-line activity. 
 

Practical significance:
 
Practical significance relates to whether the result from a statistical hypothesis test is useful or not.
Hypothesis testing tests for statistical significance. It means the effect observed in the sample was unlikely to occurred due to chance alone. In other words, it would be very unlikely to see what was observed in the sample if the null hypothesis is true.
 
Example:
If a Call Centre claims their average wait time is 30 seconds. We decide to test:
                Ho: mean=30
                Ha: mean>30
We find X (sample mean) = 30.6, and a p-value of 0.002
 
We decided to test that the population mean is 30 and the alternate hypothesis is greater than 30. We find a sample mean of 30.6 sec and get the resulting p-value is 0.002. p-value size shows very strong evidence against the null hypothesis, in this case population mean is greater than 30sec, but in the sample we observed in the sample mean is 30.6, a difference of only .6 sec from the null hypothesis. In this situation most people would feel the difference of 0.6 sec doesn’t really have any practical meaning. But perhaps the company feel differently, the company want to make sure the wait time is no more than 30sec on average and so they feel 0.6 difference is important, we simply don’t know. We can’t say in statistics that this p-value is very small, giving strong evidence against the null hypothesis. We leave up to others to decide that is practical importance. How could be such a strong evidence against the null hypothesis if the difference from the hypothesized is so small.
 
Statistical significance is strongly related to sample size. If the sample size is large enough, even tiny differences from the hypothesized value will be found statistically significant. If the sample size is small, then it can be difficult to find the statistically significant difference.
 
In statistics we determine if there is Statistical significance and let experts in the field of interest determine whether the results have any practical importance. In addition to the results of hypothesis test, it is best to also report an appropriate confidence interval.
 
The interval illustrates the size of the effect and can help to determine if the effect has any practical significance.