Cluster sampling is a technique used when it becomes difficult to study the targetpopulation spread across a wide area and simple random sampling cannot beapplied. Cluster Sample is a probability sample where each sampling unit is acollection, or cluster of elements. Systematic sampling is a statistical techniquewhere elements are selected from an ordered sampling frame. In systematicsampling, the list is progressed in a circular manner so once you reach the end of the list,it is progressed from the top again. The best example for systematic sampling is equal probability method.

They are not different but the terms are used in different contexts. Mean isgenerally referred when talking about a probability distribution or samplepopulation whereas expected value is generally referred in a random variablecontext.

For Sampling Data
Mean value is the only value that comes from the sampling data.Expected Value is the mean of all the means i.e. the value that is built frommultiple samples. Expected value is the population mean.For Distributions
Mean value and Expected value are same irrespective of the distribution, underthe condition that the distribution is in the same population.

P-value is used to determine the significance of results after a hypothesis test instatistics. P-value helps the readers to draw conclusions and is always between 0and 1.

• P- Value > 0.05 denotes weak evidence against the null hypothesis whichmeans the null hypothesis cannot be rejected.
• P-value <= 0.05 denotes strong evidence against the null hypothesiswhich means the null hypothesis can be rejected.
• P-value=0.05is the marginal value indicating it is possible to go either way.

No, they do not because in some cases it reaches a local minima or a localoptima point. You don’t reach the global optima point. It depends on the data andstarting conditions

Let’s suppose you are being tested for a disease, if you have the illness the test will end up saying you have the illness. However, if you don’t have the illness-5% of the times the test will end up saying you have the illness and 95% of thetimes the test will give accurate result that you don’t have the illness. Thus thereis a 5% error in case you do not have the illness.

Out of 1000 people, 1 person who has the disease will get true positive result.
Out of the remaining 999 people, 5% will also get true positive result.
Close to 50 people will get a true positive result for the disease.

This means that out of 1000 people, 51 people will be tested positive for thedisease even though only one person has the illness. There is only a 2%probability of you having the disease even if your reports say that you have thedisease.