Question

 

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?

Answer 1

Answer 1. There are four different data types are used.

Nominal, Ordinal, Interval and Ratio

Difference is that:

1. The order of vales is known in Ordinal, interval and ration data but not in nominal data.

2. Arithmatic operations like addition and substraction is not possible with nominal and ordinal data but possible with interval and ration data.

3. Multiplication and division possible with only ratio data.

4. Only ratio data contains absolute zero value, others do not.

5. Mean value can be found with interval and ratio data only.

6. Median value is possible with interval, ordinal and ratio data only.

7. Difference between values can be quantified using interval and ratio data only.

Answer 2.

In statistics population word is used for entirety of anyting (people, objects etc.).

Sample is a small group of people or anyting who actually participate in the experiment and it is selected to represent the whole population. The results of the experiment done on the sample will be applicable for whole population.

Answer 3. Hypothesis testing is conducted to obtain the sufficient statistical evidence towards a certain belief about the population. It is done on samples.

Answer 4. Confidence interval is basically a specified range in which the value of a population parameter lies.Confidence level is the pecentgae that the the confidence interval contain the population mean, If random samples are drown repetadely.

95% confidence level means we are 95% sure that the true value of the population mean will lie in the specified range.

Answer 5.

A. Null hypothesis (H₀) is the statement that there is no statistically significant difference between two groups or specified population.

B. It is contradiction of null hypothesis. It says that observations are affectyed some real effect.

C. Type I error: It is also known as false positive. It can be defined as incorrectly rejecting the null hypothesis even if it is true.

D. Type II error: It is also known as false nagative. It can be defined as incorrectly not rejecting the null hypothesis even if it is false.

E. p-value is important to judge the significance of results. It is usefull in identifying the likelihood of sample coming from a population given by null hypothesis.