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Course name: phcCourse number: 121
Assignment title or task: (You can write a question) Week 4 AssignmentStudent name: Khulud ahmed algarniStudent number (ID): 160175098
Submission date: 4/10/2018
To be filled in by the instructor only
Instructor name: Distinguish between different types of data
What is the difference between a population and a sample in statistics?
Sample

1- Samples may be random or not; within random samples, there are many subtypes (simple random sampling, cluster sampling, stratified sampling, etc). Random samples have many nice properties, but it is often difficult or impossible to get a random sample.

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2- A sample is a part of a population used to describe the whole group.

population

A populace incorporates the majority of the components from an arrangement of information.
An example comprises at least one perceptions drawn from the populace.

What is the purpose of hypothesis testing?
To decide whether the difference between population parameter , the sample statistic is due to chance.

AND To verify the hypotheses expected by the researcher and to ascertain whether it is true or false
We make factual examinations on concentrates that we configuration to assess theories.
The objective of a speculation is to think of a brief clarification about something you have watched, to be the theory substantial, it ought to be tried utilizing reliant and autonomous factors, the factual examinations and theory tests enable us to choose if or not we have bolster for our thoughts and furthermore likelihood of a specific speculation would be worthy at a specific pertinence level or not.
As it were, the procedure associated with making sense of if our assumption is correct or wrong and if my comprehension of the information introduced is proper known as speculation testing, it tends to be utilized in logical analyses and different fields as a method for choosing clever responses and furthermore look at the probability of the invalid theory with information and to settle on a choice about the estimation of a particular populace in light of test information.

How to interpret confidence intervals and confidence levels?
Each Confidence interim has a related Confidence Level:
• Confidence interim in Statistics is a kind of range gauge for a populace parameter in view of at least one examples. For example in the event that we need to gauge the normal tallness of all young USA young men matured 15 from an example of one hundred 15 years of age youngsters.
• The Confidence Level related to this interim would disclose to us the level of every single conceivable example that can be relied upon to incorporate the genuine populace parameter. The standard qualities are 90%, 95% and 99%.

Define:
Null hypothesis
is a sort of speculation utilized in measurements that recommends that no factual importance exists in an arrangement of given perceptions. The invalid theory endeavors to demonstrate that no variety exists between factors or that a solitary variable is the same than its mean. It is ventured to be valid until the point that measurable proof invalidates it for an elective speculation
Alternative hypothesis
is the speculation utilized in theory testing that is in opposition to the invalid speculation. It is generally taken t o be that the perceptions are the consequence of a genuine impact (with some measure of chance variety superposed).

Type I error
When you demand that your examination speculation is genuine “which is false”, at that point you choose to dismiss the invalid theory as opposed to holding the invalid speculation when, indeed, it is valid, at that point you have committed an error which known as (Type I blunder).

Type II error
When you couldn’t have enough proof to demonstrate your exploration speculation “which is valid”, at that point you choose to hold the invalid theory as opposed to dismissing the invalid theory when, truth be told, it is false, at that point you have committed an error which known as (Type II mistake).

Why the p-value is important?
Everybody realizes that you utilize P esteems to decide measurable centrality in a speculation test. Truth be told, P esteems regularly figure out what ponders get distributed and what ventures get financing.

It causes us to pick which of the two theories (research or invalid) is more adequate, in light of the fact that in view of the p-esteem that we ascertained we will dismiss or acknowledge the invalid speculation.-
– It additionally the likelihood of you acquiring your example of results if the invalid speculation is valid
In speculation testing the most vital thing we indicate the invalid theory
1) P esteem under 0.05 reveal to us that information is exceptionally noteworthy
2) P esteem in excess of 0.05 reveal to us that information is chosen haphazardly
3) Decision job implies what size of P esteem that we will specify(less than 0.05 particular, more than 0.05 by possibility).

Reference
Dancey C, Reidy J, Rowe R.(2012).Statistics for the Health Sciences: A Non-Mathematical Introduction.(1st ed.).London, England: SAGE Publications