Lecture 7 Constructing hypotheses Research Methodology Lecture 7 Constructing hypotheses Mazhar Hussain Dept of Computer Science ISP,Multan Mazhar.hussain@isp.edu.pk

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

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’

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

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

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

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.

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.

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

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

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

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.

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

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

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

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

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

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.

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

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

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

Hypothesis testing H0 = Defendent is Innocent

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

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

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%

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

Example - Biometrics Enrollment What kind of errors the system can make? http://www.idteck.com/support/biometrics.asp

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

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

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 http://en.wikipedia.org/wiki/Type_I_and_type_II_errors http://www.intuitor.com/statistics/T1T2Errors.html http://www.fingerprint-it.com http://fingerchip.pagesperso-orange.fr