Hypothesis Assumption and Decision Making.pptx

In research, hypothesis formulation, assumption testing, and decision-making are critical components that guide the scientific process. This presentation provides an in-depth overview of these concepts, their importance, and how they are applied in various research contexts.

1. HYPOTHESIS ASSUMPTION AND DECISION MAKING Guided By:- Dr. NAVEEN KUMAR VISHWAKARMA Department of Biotechnology, GGV Bilaspur,C.G Presented By:- Firdous Ashraf Pratibha Jha Priyanka Munda Khusboo Mahesh Archie Kurrey

2. CONTENT- HYPOTHESIS TYPES OF HYPOTHESIS ASSUMPTION TYPES OF ASSUMPTION DIFFERENCE BETWEEN HYPOTHESIS AND ASSUMPTION DECISION MAKING

3. Hypothesis

4. Origin of hypothesis The word hypothesis is derived from the Greek word - ' hypotithenai ' . The word hypothesis consists of two words 'Hypo' and 'thesis'. 'Hypo' means to put under or suppose. 'Thesis' means placing or proposal . So the word "Hypothesis" means the guesses to solve the research problem.

5. Definition of Hypothesis "It is a tentative supposition or provisional guess which seems to explain the situation under observation." - James E. Greighton Hypothesis is a formal statement that presents the expected relationship between an independent and dependent variable. (Creswell,1994) Hypothesis is stated before the data collection. It is the powerful tool in research process (helps researcher to relate theory to observation)

6. Importance of a hypothesis To the point enquiry Separating Relevant from Irrelevant observation Selecting required facts Direction of research Prevent blind research Provide answer for question It provide link between theories and actual practical Save money ,time ,energy Proper data collection Proper conclusion

7. CHARACTERISTICS OF A GOOD HYPOTHESIS Conceptual clarity & availability of techniques Empirical referent & economical Objectivity & consistency Specificity & simplicity Relevant & purposiveness Testability & verifiability

8. SOURCES OF HYPOTHESIS Experience of researcher Review of literature Interaction with knowledgeable persons Knowledge of culture and society Creative thinking and imagination of researcher Observation

9. Types of Hypothesis Simple Hypothesis Complex Hypothesis Empirical Hypothesis Null Hypothesis Alternative Hypothesis Logical Hypothesis Statistical Hypothesis

10. Types of Hypothesis Simple Hypothesis : Simple hypothesis is that one in which there exists relationship between two variables one is called independent variable or cause and the other is dependent variable or effect. For ex : If you eat more vegetables , you will lose weight faster Complex hypothesis : A complex hypothesis predicts the relationship between two or more independent and dependent variables. For ex : Eating more vegetables and fruits will lead to loss in your weight

11. Empirical Hypothesis : An empirical hypothesis is a theory that is based on observations or experiments from the past. It is also referred to as a working hypothesis.

12. Null Hypothesis : The null hypothesis in statistics states that there is no difference between groups or no relationship between variables. It is one of two mutually exclusive hypotheses about a population in a hypothesis test. For example : When your sample contains sufficient evidence, you can reject the null and conclude that the effect is statistically significant.

13. Alternative Hypothesis : This is also known as the claim. This hypothesis should state what you expect the data to show, based on your research on the topic. This is your answer to your research question. Logical Hypothesis : A logical hypothesis is a hypothesis that cannot be tested, but has some logical basis underpinning our assumptions. For ex : Dinosaurs closely related to Aligators probably had green scales because Aligators have green scales. However, as they are all extinct, we can only rely on logic and not empirical data.

14. Statistical Hypothesis : A statistical hypothesis utilizes representative statistical models to draw conclusions about broader populations. For ex : Human Sex Ratio. The most famous statistical hypothesis example is that of John Arbuthnot’s sex at birth case study in 1710. Arbuthnot used birth data to determine with high statistical probability that there are more male births than female births. He called this divine providence, and to this day, his findings remain true: more men are born than women.

15. Hypothesis Testing In hypothesis testing,we decide whether to reject or accept a particular value of a set of particular values of a parameter or those of several values .It is seen that , although the exact value of a parameter may be unknown ,there is often same idea about the true value.The data collected from the sample helps us in rejecting or accepting our hypothesis.In dealing with problems of Hypothesis Testing ,we try to arrive at the right decision about the pre stated hypothesis .

16. ASSUMPTION

17. Origin Of Assumption

18. ASSUMPTIONS Assumptions are statements that are taken for granted or are considered true, even though they have not been scientifically tested.Assumptions are principles those are accepted as being true based on logic or reasons but without proof or verification.

19. IMPORTANCE OF ASSUMPTIONS IN RESEARCH Assumptions work as a foundation or base for researcher. Selection of research topic can be based on written assumption or assumptions of the previous studies. Assumptions helps in research process and conclude the research study. Verified and tested assumptions expand the body of knowledge.

20. TYPES OF ASSUMPTION Universal Assumption Based on theories Empirical based assumption Research/ methodology Assumption Warranted Unwarranted

21. 1.UNIVERSAL ASSUMPTIONS These are beliefs that assumed to be true by a large part of society or universe. Testing of such assumption is not always possible EXAMPLE; Divine power controls the universe.

22. 2.ASSUMPTIONS BASED ON THEORIES It may also be drawn from theories. If a research study is based on a theory, the assumption of particular theory may become assumption of that particular research study. EXAMPLE; Physics assumptions, In physics, scientists assume that the gravitational forces on a falling ball are related to those on other falling objects. They also assume that gravity works the same way every time, regardless of the object or the moment.

23. 3. EMPIRICAL BASED ASSUMPTIONS These are derived from previous research studies. They are therefore considered the most reliable. EXAMPLE; Use of drug warfarin for ischemic heart disease or coronary artery bypass grafting surgery patients improves blood circulation and prevents thrombus formation. 4.RESEARCH / METHODOLOGICAL ASSUMPTIONS Researcher has to formulate some of the methodological assumptions to conduct a research study. EXAMPLE; Participants will be participating in the study willingly and respond to the research tools honestly.

24. 5.WARRANTED ASSUMPTIONS These type of assumptions stated along with the proof or evidences to support. EXAMPLE; Breakfast before school, the assumption that children who eat breakfast will be able to concentrate and complete tasks. 6.UNWARRANTED ASSUMPTIONS These assumptions do not have supportive evidence or proof. Sometimes, it will be difficult to fulfill these kind of assumptions. EXAMPLE; Biases, Believing that someone who dresses casually at a professional event is not successful or serious about their caree

25. HYPOTHESIS vs ASSUMPTION ASPECTS HYPOTHESIS ASSUMPTION Definition A testable statement or claim about a population parameter that can be evaluated using statistical methods. A statement or condition accepted as true without proof, assumed for the purpose of analysis. Purpose Hypothesis are statements that are tested using data to make inference about a population. Assumptions provide the foundation or starting point for statistical methods, models and tests. Nature Hypothesis are specific, testable prediction or claims that can be supported or rejected based on sample data. Assumption are typically general condition that are taken as given in the model (e.g., normality, independence). Example The mean of the population is equal to a specific value. -There is a relationship between two variables. Data is normally distributed. -Observations are independent. -Variance is constant (homoscedasticity).

26. ASPECTS HYPOTHESIS ASSUMPTION Testing / Verification Hypothesis are directly tested using statistical tests(e.g., t-test, chi-squared test) to determine if they can be accepted or rejected. Assumption are not directly tested in hypothesis testing but may affect the validity of the test. Some assumption can be tested indirectly (e.g., normality). Role in analysis Hypothesis define the research question and determine the outcomes of the statistical analysis (accept or reject). Assumptions define the model framework and the conditions under which the analysis is valid. Example in context Null hypothesis: The average income of the population is $50,000. - Alternative hypothesis: The average income of the population is not $50,000. The data follows a normal distribution (assumption for many parametric tests). - The sample is random (assumption for unbiased estimates).

27. DECISION MAKING Decision making is the process of identifying and choosing between alternatives to achieve a specific objective or solve a problem. It involves analyzing available information, considering possible outcomes, and selecting the most appropriate course of action.

28. Decision-Making Process in Biostatistics In biostatistical decision making, the process typically involves the following steps: Step 1: Problem Formulation The first step is to define the problem clearly. For example: Epidemiological studies : What are the risk factors for a particular disease? Step 2: Data Collection Data can come from various sources: Epidemiological studies : Cohort studies, case-control studies, cross-sectional studies. Public health data : Surveys, national health data.

29. Step 3: Data Analysis Biostatistical analysis tools are used to analyze the data. Common methods include: Hypothesis Testing : To assess if there is sufficient evidence to support or reject a hypothesis (e.g., t-tests, chi-square tests, ANOVA). Regression Analysis : To explore relationships between variables (e.g., logistic regression for binary outcomes). Bayesian Analysis : To incorporate prior knowledge and update beliefs as new data are collected.

30. Step 4: Decision Criteria Decision criteria are established to guide the decision-making process. These could include: Confidence Intervals : Used to estimate the range of plausible values for a parameter, providing a sense of uncertainty around the estimate. Effect Size : The magnitude of the treatment effect, which helps to interpret the practical significance of results. Step 5: Make the Decision Based on the analysis, the biostatistician makes a decision. For example- in clinical trials, this could be whether a drug is safe and effective, or in epidemiology, whether a risk factor significantly influences disease occurrence.

31. Uncertainty : Data is often imperfect, and decisions must be made under uncertainty. Decisions can be influenced by sampling variability, measurement errors, or confounding factors. Ethical Issues : Especially in clinical trials, there may be ethical dilemmas in making decisions that affect patient outcomes, such as determining whether a treatment should be administered to certain individuals. Data Quality : Incomplete, biased, or misreported data can significantly affect decision-making. Risk Management : In healthcare and public health, decisions often involve balancing risks, such as false positives and false negatives in diagnostic tests, or underestimating the side effects of treatments. Challenges in Decision Making in Biostatistics

32. Reference P. N. Arora and P. K. Malhan , Biostatistics, (Latest ed) Himalaya PublishingHouse . Biostatistics: a Foundation for Analysis in the Health Sciences, (Latest ed)., Wiley. 3. P. N. Arora and P. K. Malhan , Biostatistics, (Latest ed). Himalaya PublishingHouse .

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