1. Lecture 7 Constructing hypotheses
Research Methodology
Lecture 7 Constructing hypotheses
Mazhar Hussain
Dept of Computer Science
ISP,Multan

2. Road Map Introduction Chosing your research problem
Chosing your research advisor
Literature Review
Plagiarism
Variables in Research
Construction of Hypothesis
Research Design
Writing Research Proposal
Writing your Thesis
Data Collection
Data Representation
Sampling and Distributions
Paper Writing
Ethics of Research

3. Hypothesis Hypothesis
Brings clarity, specificity and focus to research problem
Possible to conduct a study without hypothesis as well
Hypothesis – how to construct
Arise from ‘hunches’ or ‘educated guesses’

4. Hypothesis - Examples Betting on a horse race Distribution of smokers
Hunch – Horse#6 will win
Hunch is true or false – Only after the race
Distribution of smokers
Hunch – more male smokers at your workplace than female smokers
Test the hunch – ask them
Conclude – hunch was right or wrong

5. Hypothesis - Examples Public health
A disease is very common in people coming from a specific sub-group of population
To find every possible cause – enormous time and resources
Perform a study – collect information to verify your hunch
Verificiation – hunch correct or not

6. hypothesis
Researcher – does not know about a phenomenon, situation or a condition
But – does have a hunch, assumption or guess
Conclude through verification
Hunch may be
Right
Wrong
Partially right

7. Hypothesis - definitions
A tentative statement about something, the validity of which is usually unknown
A proposition that is stated in a testable form and that predicts a particular relationship between two or more variables.
A hypothesis is written in such a way that it can be proven or disproven by valid and reliable data – it is in order to obtain these data that we perform our study.

8. hypothesis
From the definitions, a hypothesis has certain characteristics:
It is a tentative proposition
Its validity is not known
In most cases, it specifies a relationship between two or more variables.

9. Hypothesis - considerations
A hyothesis should be simple, specific and clear
No ambiguity in the hypothesis – makes verification difficult
Unidimensional – should test one relationship at a time
Must be familiair with the subject area (literature review) before suggesting the hypothesis

10. Hypothesis - considerations
The average age of male students in the class is higher than that of female students
Clear
Specific
Testable

11. Hypothesis - considerations
A hypothesis should be capable of verification
Data collection and analysis
Hypothesis cannot be tested?
May forumulate hypothesis for which methods of verification not available
You may end up developing a technique
A hypothesis should be operationalisable
Expressed in terms that can be measured

12. Your hypothesis which you want to test
Type of hypothesis
Categories of hypothesis
Research hypothesis
Alternative hypothesis
Your hypothesis which you want to test
Specify the relationship that will be considered as true in case the research hypothesis proves to be wrong.

13. Ways of formulating hypothesis
There is no significant difference in the proportion of male and female smokers in the study population
A greater proportion of females than males are smokers in the study population
A total of 60% of females and 30% of males in the study population are smokers
There are twice as many female smokers as male smokers in the study population

14. Ways of formulating hypothesis
Hypothesis of No Difference
When you formulate a hypothesis stipulating that there is no difference between two situations, groups or outcomes
There is no significant difference in the proportion of male and female smokers in the study population

15. Ways of formulating hypothesis
Hypothesis of Difference
A hypothesis in which a researcher stipulates that there will be a difference but does not specify its magnitude
A greater proportion of females than males are smokers in the study population

16. Ways of formulating hypothesis
Hypothesis of Point-Prevalence
A researcher has enough knowledge about the behaviour/situation
Able to express the hypothesis in quantitative units
A total of 60% of females and 30% of males in the study population are smokers

17. Ways of formulating hypothesis
Hypothesis of Association
Expressed as a relationship
Twice as many female smokers as male smokers

18. Hypothesis testing Hypothesis testing - H0 Null hypothesis
Usually corresponds to a default "state of nature", for example "this person is healthy", "this accused is not guilty" or "this product is not broken".
Alternative hypothesis
Negation of null hypothesis, for example, "this person is not healthy", "this accused is guilty" or "this product is broken ".
Errors depend directly on null hypothesis.

19. Hypothesis testing True state of nature H0 is True H0 is False
Reject H0
Accept H0
Your Decision

20. Hypothesis testing True state of nature H0 is True H0 is False
Reject H0
Type I error
Correct Decision
Accept H0
Type II error
Your Decision

21. Hypothesis testing H0 = This person is healthy
H0 is True
H0 is False
Reject H0
Type I error
Correct Decision
Accept H0
Type II error
Hypothesis testing
H0 = This person is healthy
Telling the person that he is sick when infact he was healthy
Type I error
Correct
Telling the person that he is sick when infact he was sick
Telling the person that he is healthy when infact he was sick
Type II error
Telling the person that he is healthy when infact he was healthy
Correct
Traditionally probability of type I errors is denoted by α and that of type II errors by β

22. Hypothesis testing
H0 = Defendent is Innocent

23. Example – Airport travelers
True state of nature
Innocent
Terrorist
False positive
True positive
True Negative
False negative
Your Decision

24. Example: Face Detection
True Positives
False Negative
False Positives
True Negative (Rest of the image)

25. Example: Face Detection
How many faces were detected out of total?
Recall = 3/4= 75%
Did system detected extra objects other than faces?
Precision = 3/6 = 50%

26. Example - Biometrics Biometric access control system Enrollment
Finger print, iris, face, hand geometry etc.
Enrollment
Enroll all the authorized users – take their finger prints, facial images or iris scans etc.
Validation
A person arrives
Take data (finger print, iris, face)
Compare with database
If matched with an individual – Allow
Else - Decline

27. Example - Biometrics Enrollment
What kind of errors the system can make?

28. Example
The FRR is the frequency that an authorized person is rejected access
The FAR is the frequency that a non authorized person is accepted as authorized

29. Example - Biometrics Challenge
How to find a similarity threshold value for acceptance/rejection
Find system response to a large number of inquires from authorized as well as unauthorized users.
Record similarity scores of authorized and unauthorized cases
Plot respective histograms/distributions

30. References Research Methodology, Ranjit Kumar, Chapter 6
The material in these slides is based on the following resources.
References
Research Methodology, Ranjit Kumar, Chapter 6