The document provides an overview of a research methodology and statistics course. It outlines the course objectives, which are to provide an understanding of research methodology and enable students to apply it to forensic science. It also lists the course outcomes, which are for students to be able to recall research objectives and types, appraise sampling methods and research design, conduct primary and secondary data collection, and perform descriptive and inferential statistics. The document further details the topics to be covered in each unit, such as introduction to research methodology, sampling design, data collection, and statistics. It provides teaching methods, timelines and an overview of the content to be covered in each unit.
Explains the different methods of Sampling with diagram. In statistics, quality assurance, and survey methodology, sampling is the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt for the samples to represent the population in question.
Explains the different methods of Sampling with diagram. In statistics, quality assurance, and survey methodology, sampling is the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt for the samples to represent the population in question.
The process of using a small number of items or parts of larger population to make a conclusions about the whole population.
Sampling is the process of selecting representative units from an entire populations of a study .
Sampling is a technique of selecting a subset of the population.
EXAMPLE – While cooking rice to see whether the rice are cooked or not we never see each and every grain of rice. only a sample of rice grain is checked to make the decision regarding the cooked or the uncooked rice
A sample should be reliable.
A sample should be economical.
A sample should be goal oriented.
A sample should be appropriate in size.
A sample should be free from bias and errors.
A sample should be true representation of population.
NATURE OF THE RESEARCHER-
Inexperienced investigator
Lack of interest
Lack of honesty
Lack of adequate resources
Inadequate supervision
NATURE OF SAMPLE –
Inappropriate sampling technique
Sample Size
Defective sampling Frame
A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.
Sampling means selecting the group that researcher will actually collect data from in research. It attempts to collect samples that are representative of the population.
sampling design methods in research it discuses about what is research, types of research and types of sampling design it considering about probability and non-probability sampling
Understanding The Sampling Design (Part-II)DrShalooSaini
This Power Point Presentation has been made while referring to the research books written by eminent, renowned and expert authors as mentioned in the references section. The purpose of this Presentation is to help the research students in developing an insight about the Sampling Design(Part-II).
The process of using a small number of items or parts of larger population to make a conclusions about the whole population.
Sampling is the process of selecting representative units from an entire populations of a study .
Sampling is a technique of selecting a subset of the population.
EXAMPLE – While cooking rice to see whether the rice are cooked or not we never see each and every grain of rice. only a sample of rice grain is checked to make the decision regarding the cooked or the uncooked rice
A sample should be reliable.
A sample should be economical.
A sample should be goal oriented.
A sample should be appropriate in size.
A sample should be free from bias and errors.
A sample should be true representation of population.
NATURE OF THE RESEARCHER-
Inexperienced investigator
Lack of interest
Lack of honesty
Lack of adequate resources
Inadequate supervision
NATURE OF SAMPLE –
Inappropriate sampling technique
Sample Size
Defective sampling Frame
A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.
Sampling means selecting the group that researcher will actually collect data from in research. It attempts to collect samples that are representative of the population.
sampling design methods in research it discuses about what is research, types of research and types of sampling design it considering about probability and non-probability sampling
Understanding The Sampling Design (Part-II)DrShalooSaini
This Power Point Presentation has been made while referring to the research books written by eminent, renowned and expert authors as mentioned in the references section. The purpose of this Presentation is to help the research students in developing an insight about the Sampling Design(Part-II).
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
2. Course Objectives
To provide an understanding of research methodology
To enable students to apply research methodology to the field of
forensic science
Course Outcomes
After the successful completion of the course, the student will
be able to:
CO1 : to recall and recognize the objectives, motivations, and types
of research
CO2 : appraise the methods of sampling and research design
CO3 : develop and execute primary and secondary data collection
CO4 : Test and validate descriptive and inferential statistics on
continuous and categorical
3. Unit /Topic No. OF
HOURS
TEACHING METHODOLOGY TIME OF
COMPLETION
Unit 1: Introduction to Research
Methodology
15 Mapped to Ms. Aditi
Unit 2: Research and Sampling Design
(Steps in Sampling Design; Criteria of
Selecting a Sampling Procedure; 10
Characteristics of a Good Sample Design;
Types of Sample Designs; Hypothesis
formulation and testing)
10 Co shared with Ms. Aditi
Participatory TL: Interactive Lecture,
Guided library work, Technical
presentation
Aug 14- Aug 19, 2023
Unit 3: Data Collection 8 Participatory TL: Interactive Lecture,
Guided library work, Technical
presentation
Sep 4- Sep 9, 2023
Unit 4: Descriptive Statistics and Inferential
Statistics
12 Participatory TL: Interactive Lecture,
,Guided library work,
Technical presentation
Experiential TL: workshop
Oct 30 – Oct 31, 2023
4. Unit 2: Research and
Sampling Design
Unit 3: Data Collection Unit 4: Descriptive Statistics
and Inferential Statistics
Sampling Design; Criteria of
Selecting a Sampling
Procedure; Characteristics of a
Good Sample Design; Types of
Sample Designs; Hypothesis
formulation and testing.
Sampling Design; Criteria of
Selecting a Sampling
Procedure; 10 Characteristics
of a Good Sample Design;
Types of Sample Designs;
Hypothesis formulation and
testing.
Statistics in research;
Measures of Central Tendency;
Measures of Dispersion;
Measures of Asymmetry;
Measures of Relationship;
Simple Regression Analysis;
Multiple Correlation and
Regression; t-test; Chi square
test; ANOVA; Introduction to
Statistical Package for Social
Sciences (SPSS)
5.
6.
7. Sampling techniques are methods used to select a
representative subset (sample) from a larger population
for the purpose of conducting research, analysis, or
making inferences about the entire population.
8. Some of the key advantages of sampling include:
Time and Cost Efficiency
Feasibility
Accuracy if representative of the population
Ethical Considerations (potential harm or invasion of
privacy)
Practicality for Data Analysis
Generalization (in a representative and unbiased)
Accessibility
9. Important terminology
Population.
• The entire group or set of individuals, items, or elements from which the
sample is drawn, and the results are generalized.
Sampling Frame
• A list or representation of all the elements in the population from which
the sample is drawn. It is the actual source used to select the sample.
Sample
• A subset of the population that is selected for study or analysis. The
sample represents the larger population, and conclusions drawn from the
sample are extrapolated to the population.
Sampling Unit
• The individual element or item in the population that can be selected in
the sampling process. It can be a person, household, product, or any other
discrete entity.
10. Criteria of Selecting a Sampling Procedure
Representativeness: sample is a true reflection of the
population's characteristics
Randomness : each individual in the population has an
equal chance of being selected for the sample/avoid bias
Precision: close the sample's results are to the true
population values
Feasibility: The chosen sampling procedure should be
feasible in terms of time, budget, and resources.
Research Objectives
Accessibility:availability of a sampling frame
Homogeneity
Ethical Considerations
13. Sampling
technique
Advantages Disadvantages
Simple
Random
Sampling
1. It is hassle-free method of sampling
population is homogeneous.
2. b. There is no chance of personal bias of the
researcher to influence
3. requires no computation of any sort
1. It cannot be used in heterogeneous population.
2. It cannot be used where researcher wants to
conduct a mini-comparison within the universe by
studying the sample in divisions.
3. It requires basic knowledge of the universe, to
make a list to be able to choose from
Systemic
Sampling
1. This method is easy to understand and use.
2. b. This method involves least number of
steps.
3. There is least chance of influence of
personal bias of researcher.
4. No knowledge of the universe is required
before sampling
1. Every unit in the universe does not have equal
chances of being selected in the sample as the
selection depends on the ‘n’ number chosen.
2. It is not an effective sampling method in case of
heterogeneous population.
14. Sampling
technique
Advantages Disadvantages
Stratified
Sampling
1. There is better representation of the
different characteristics of the population.
2. The researcher can use results from
different strata to compare results within
the universe.
1. it involves more time as samples are to be taken
out from each strata to form the final sample
Cluster
sampling
1. It is useful where the population is divisible
into clusters, even heterogeneous clusters.
2. useful in large geographical areas.
3. As division of clusters is not dependent on
them being homogeneous. Therefore, more
than one characteristic can be studied in
one cluster.
4. There is no need to have a prior knowledge
of the population.
1. The clusters are not equal in size, so the final
sample may not represent the population
proportionately. Even if the study is conducted in
multi- phase manner, the clusters do not offer a
comparative analysis.
2. There is a possibility that a same person may form
part of more than one cluster. This will lead to
over representativeness.
3. there is a possibility that some clusters may be
homogeneous while other may be heterogeneous
15.
16. Sampling
technique
Advantages Disadvantages
Convenience
sampling
1. suited for those researches
which are preliminary or pilot
projects, and which will be
supplemented with further
probability sampling research.
1. Low Diversity: tends to attract participants who are
readily accessible or willing to participate, leading to a
sample that lacks diversity in terms of demographics,
opinions, or experiences.
Purposive
sampling
1. It is easy on the pocket, as the
researcher chooses the units
himself/herself. There is no cost
involved in selecting units for
sample.
2. No prior knowledge of the
universe is required.
1. Representativeness of the sample is questionable.
2. It is not useful in cases of heterogeneous population.
3. Sampling may be influenced by the personal bias of the
researcher
17. Sampling
technique
Advantages Disadvantages
Quota sampling 1. The advantage of quota
sampling is its cost and time
efficacy.
2. It is one of the most effective
sampling, for small scale as
well as large scale sampling.
1. Lack of Representativeness
2. Determining the appropriate quotas can be challenging,
especially if the characteristics being targeted for quota
setting are interconnected or difficult to define
Snow ball
sampling
1. Access to Hard-to-Reach
Populations:
2. Cost-Effective
3. Quick Data Collection
1. Limited Control: Researchers have limited control over
the sampling process, as it relies heavily on
participants' referrals.
18. Principles and Precautions
of Sampling
The universe must be clearly defined.
The sampling units must be distinct and independent of
each other.
A clearly chalked out sampling design ensures
predetermined steps, and also encompasses planning for
contingencies.
Sampling must be done in an unbiased, objective and
systematic manner.
The objective of the research must be kept in mind while
sampling.
Arbitrary alterations must be avoided during sampling.
Sample size must be chosen in accordance with the nature of
study, i.e. qualitative or quantitative, and taking into
consideration the size of the universe.
19. Principles and Precautions
of Sampling
❑ The cost and time factor is an important influencing
factor in research. It is advisable to not see these factors
as an impediment to research, but to utilise them in the
most efficient way possible.
❑ Ease of contacting the respondents is another
important factor that is to be taken into consideration
while sampling.
❑ Even with the advent of technology, care must be taken
by the researcher that the selected respondents are
source of objective, unbiased answers.
❑ It should also be ensured to maximum possible extent
that the potential respondents are not being forced for
participation in the research.
❑ Sampling errors (in sample size, proportions) must be
avoided as much as possible.
20. Characteristics of a Good Sample
Design
Representativeness Randomization Adequate Sample Size
Sampling Frame: A clear
and accurate sampling
frame, which is a list of all
the potential individuals
or units in the population
Sampling Method:
should align with the
research objective
Sample Variability:
considers the variability
within the population.
Avoidance of Bias: non-
response bias, selection
bias, or measurement
bias.
Ethical Considerations:
Participants' rights and
informed, privacy and
confidentiality
Clear Sampling Plan
Pilot Testing: help
identify any potential
issues or areas for
improvement in the
sampling process.
21. HYPOTHESIS
A hypothesis is a specific, testable, and
falsifiable statement or proposition that
predicts a relationship between variables or
anticipates an outcome in a research study.
It serves as a tentative explanation that
researchers aim to confirm or reject through
empirical observation and analysis.
Testable
Specific
(clear and precise)
Falsifiable
(capable of being
proven wrong through
evidence)
Predictive
(Relationship or effect
between variables)
Empirical
(Observations,
existing theories, or
logical reasoning)
Verifiable(observable
and measurable
results)
22. Hypothesis Formulation
Identify the
Research
Problem
Literature Review
(understand gap
in literature)
Formulate the
Hypothesis: Null
and alternative
hypothesis
Specify Variables:
Clearly define the
independent
variable(s) and the
dependent
variable(s)
Directional vs.
Non-Directional
Hypotheses
Research Question: Does the new drug lead to a decrease in blood pressure?
Example of a Directional Hypothesis:
The new drug leads to a decrease in blood pressure.
Example of a Non Directional Hypothesis:
There is a relationship between new drug and blood pressure.
23.
24.
25. Types of Hypothesis
•Simple hypothesis: This type of hypothesis suggests
that there is a relationship between one independent
variable and one dependent variable.
Eg. "Students who eat breakfast will perform
better on a math exam than students who do not
eat breakfast.“
•Complex hypothesis: This type of hypothesis suggests a
relationship between three or more variables, such as
two independent variables and a dependent variable.
Eg. "People with high-sugar diets and sedentary
activity levels are more likely to develop
depression."
26. Null hypothesis: This hypothesis suggests
no relationship exists between two or
more variables.
Eg. "Children who receive a new
reading intervention will have no
difference in the scores.“
•Alternative hypothesis: This hypothesis
states the opposite of the null hypothesis.
Eg. "Children who receive a new
reading intervention will perform
better than students who did not
receive the intervention."
27. Statistical hypothesis: This hypothesis uses statistical
analysis to evaluate a representative sample of the
population and then generalizes the findings to the larger
group.
Eg. There is a correlation between students'
study hours and their exam scores.
Logical hypothesis: This hypothesis assumes a
relationship between variables without collecting data
or evidence.(based on logic)
Eg. If a plant is deprived of sunlight, it is
expected that its growth will be negatively
affected compared to a plant that receives
adequate sunlight.
28. This Photo by Unknown Author is licensed under CC BY
29. To write a hypothesis:
Identify what the problem is.
Make an educated guess as to what direction of the
relationship or difference is.
Identify the major variables.
The format for writing a hypothesis is . . . o If (variables),
o Then (predict the outcome of the experiment using the
dependent variable).
Eg.Observation : Chocolate may cause acne.
scientific hypothesis statement that is measurable: If a
person’s frequency of acne is related to the amount of
chocolate a person consumes, then the frequency of acne
will be 25% higher when subjects consume large amounts
of chocolate (5 chocolate bars per day) than when subjects
consume little or no chocolate.
30. As a group, create hypothesis based
on sample observations/general
hypotheses.
A few sample items from which to develop scientific
hypothesis are:
1. Salt in soil may affect plant growth.
2. Temperature may cause leaves to change color.
3. Sunlight causes fruit to ripen more quickly.
4. Plant growth may be affected by the color of the
light.
5. Bacterial growth may be affected by temperature.
6. Ultra violet light may cause skin cancer.
31. Testing of hypothesis
Formulate a
Hypothesis
(Null and
Alternative
Hypotheses)
Choose a
Significance
Level (epresents
the probability of
making a Type I
error (rejecting
the null
hypothesis when
it's actually true)
Collect Data
(hypothesis and
research design)
Perform
Statistical
Analysis
Calculate Test
Statistic: (e.g., t-
statistic, z-score,
F-statistic).
Draw a
Conclusion
Consider
Limitations
(potential
sources of error,
and the
generalizability
of findings to
population)
Report Results
(research papers,
presentations, or
other appropriate
channels.)