Answer All Questions please. D on\'t use handwriting,please. Don\'t copy and pas

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Answer All Questions please. Don't use handwriting,please. Don't copy and paste Use ypur own words. I need Reference link . (important)

(i need good answer please) in details

Q1:Distinguish between different types of data
Q2:What is the difference between a population and a sample in statistics?
Q3:What is the purpose of hypothesis testing?
Q4:How to interpret confidence intervals and confidence levels?

Q5:Define:

A. Null hypothesis
B. Alternative hypothesis
C. Type I error
D. Type II error
Q6Why the p-value is important?

Explanation / Answer

Q2) In statistics, a population is a set of similar items which is of interest for some experiment or question. A statistical population may include a group of existing objects (ex. The set of all students in a class) or a hypothetical one (ex: suppose a pond contains 100 fishes, fish is a population here). An important aim of statistical analysis is to generate information about some chosen population (1,2).

In statistics, a sample is supposed to be the subset of the population. A sample can represent a population. That is, a student, named Harry, is a sample, representing student population in class. If properly, a sample is chosen, characteristics of the entire population can be done from that sample. Suppose, Harry is an average student, he is representing that students are average in that class. So, one should select sample properly. Suppose, you select the best student of the class. But all students in a class can not be best. But, if you select an average student he can represent the class. If you select five samples, where all of them are good, so one can say that student population of that class is good in study (3).

Q3) The purpose of hypothesis testing is to determine whether there is enough statistical evidence in favor of a certain belief, or hypothesis, about a parameter.

Examples: Is there statistical evidence, from a random sample of potential customers, to support the hypothesis that more than 10% of the potential customers will purchase a new product?

Q4) The confidence interval is the plus or minus figure, reported in TV or newspaper (opinion of poll results). For example, if one uses a confidence interval of 4 and 50% percent of the sample picks an answer, one can ensure that if one had asked the question of the entire population between 46% (50-4) and 54% (50+4) would have picked that answer.

The confidence level is that, how sure one can be. It is expressed as a percentage and it represents how frequently the true percentage of the population who would have picked an answer lies within the confidence interval. The 80% confidence level means you can be 80% certain. Most researchers use the 95% confidence level.

Q5)

A)

A hypothesis is a speculation or theory based on insufficient evidence that lends itself to further testing and experimentation. With further testing, a hypothesis can usually be proven true or false. Let's look at an example. Little Susie speculates, or hypothesizes, that the flowers she waters with club soda will grow faster than flowers she waters with plain water. She waters each plant daily for a month (experiment) and proves her hypothesis true!

A null hypothesis is a hypothesis that says there is no statistical significance between the two variables in the hypothesis. It is the hypothesis that the researcher is trying to disprove. In the example, Susie's null hypothesis would be something like this: There is no statistically significant relationship between the type of water I feed the flowers and growth of the flowers. A researcher is challenged by the null hypothesis and usually wants to disprove it, to demonstrate that there is a statistically-significant relationship between the two variables in the hypothesis.

B) An alternative hypothesis simply is the inverse, or opposite, of the null hypothesis. So, if we continue with the above example, the alternative hypothesis would be that there IS indeed a statistically-significant relationship between what type of water the flower plant is fed and growth.

C)A type I error occurs when the null hypothesis (H0) is true, but is rejected. It is asserting something that is absent, a false hit.

D)A type II error occurs when the null hypothesis is false, but erroneously fails to be rejected. It is failing to assert what is present, a miss.

Q6) P-value actually tells us the probability or how likely your sample is to have come from the population followed the null hypothesis. That is what we really want to know is if the null is true. So if we got a sample and it was unlikely to have come from such a population, we can conclude that the null hypothesis is false, and will be described by the alternative hypothesis. P-value is important because, when one performs a hypothesis test in statistics, a p-value helps you to determine the significance of the result. A small p-value ( 0.05) indicates strong evidence against the null hypothesis, so one can reject the null hypothesis. Whereas, a large p-value (> 0.05) indicates weak evidence against the null hypothesis, thus, one can accept the null hypothesis (4).

Ref: 1. https://www.statistics.com/glossary&term_id=812

Ref 2: https://en.wikipedia.org/wiki/MathWorld

Ref 3: https://www.statistics.com/glossary&term_id=281

Ref 4: https://www.bmj.com/content/349/bmj.g4550.full

Ref 5: https://www.utdallas.edu/~scniu/OPRE-6301/documents/Hypothesis_Testing.pdf

Ref 6: https://study.com/academy/lesson/what-is-a-null-hypothesis-definition-examples.html

Ref 7: https://en.wikipedia.org/wiki/Type_I_and_type_II_errors

Ref 6: https://study.com/academy/lesson/what-is-a-null-hypothesis-definition-examples.html

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