The error (or disturbance) of an observed value is the deviation of the observed value from the (unobservable) true value of a quantity of interest (for example, a population mean), and the residual of an observed value is the difference between the observed value and the estimated value of the quantity of interest (for example, a sample mean).
Suppose there is a series of observations from a univariate distribution and we want to estimate the mean of that distribution (the so-called location model). In this case, the errors are the deviations of the observations from the population mean, while the residuals are the deviations of the observations from the sample mean.
A statistical error (or disturbance) is the amount by which an observation differs from its expected value, the latter being based on the whole population from which the statistical unit was chosen randomly. For example, if the mean height in a population of 21-year-old men is 1.75 meters, and one randomly chosen man is 1.80 meters tall, then the "error" is 0.05 meters; if the randomly chosen man is 1.70 meters tall, then the "error" is −0.05 meters. The expected value, being the mean of the entire population, is typically not observable, and hence the statistical error cannot be observed either.
The error (or disturbance) of an observed value is the deviation of the observed value from the (unobservable) true value of a quantity of interest (for example, a population mean), and the residual of an observed value is the difference between the observed value and the estimated value of the quantity of interest (for example, a sample mean).
Suppose there is a series of observations from a univariate distribution and we want to estimate the mean of that distribution (the so-called location model). In this case, the errors are the deviations of the observations from the population mean, while the residuals are the deviations of the observations from the sample mean.
A statistical error (or disturbance) is the amount by which an observation differs from its expected value, the latter being based on the whole population from which the statistical unit was chosen randomly. For example, if the mean height in a population of 21-year-old men is 1.75 meters, and one randomly chosen man is 1.80 meters tall, then the "error" is 0.05 meters; if the randomly chosen man is 1.70 meters tall, then the "error" is −0.05 meters. The expected value, being the mean of the entire population, is typically not observable, and hence the statistical error cannot be observed either.
Parametric and non parametric test in biostatistics Mero Eye
This ppt will helpful for optometrist where and when to use biostatistic formula along with different examples
- it contains all test on parametric or non-parametric test
Normal provability curve is one of the important topic in the Educational research.The theory of parametric tests in the inferential statistics is completely based on the NPC. Every researcher must know the characteristics of the NPC.
01 parametric and non parametric statisticsVasant Kothari
Definition of Parametric and Non-parametric Statistics
Assumptions of Parametric and Non-parametric Statistics
Assumptions of Parametric Statistics
Assumptions of Non-parametric Statistics
Advantages of Non-parametric Statistics
Disadvantages of Non-parametric Statistical Tests
Parametric Statistical Tests for Different Samples
Parametric Statistical Measures for Calculating the Difference Between Means
Significance of Difference Between the Means of Two Independent Large and
Small Samples
Significance of the Difference Between the Means of Two Dependent Samples
Significance of the Difference Between the Means of Three or More Samples
Parametric Statistics Measures Related to Pearson’s ‘r’
Non-parametric Tests Used for Inference
A hypothesis is a testable statement about the relationship between two or more variables and errors reveal about the rejection and acceptance of the statement.
Please like, comment and share
Parametric and non parametric test in biostatistics Mero Eye
This ppt will helpful for optometrist where and when to use biostatistic formula along with different examples
- it contains all test on parametric or non-parametric test
Normal provability curve is one of the important topic in the Educational research.The theory of parametric tests in the inferential statistics is completely based on the NPC. Every researcher must know the characteristics of the NPC.
01 parametric and non parametric statisticsVasant Kothari
Definition of Parametric and Non-parametric Statistics
Assumptions of Parametric and Non-parametric Statistics
Assumptions of Parametric Statistics
Assumptions of Non-parametric Statistics
Advantages of Non-parametric Statistics
Disadvantages of Non-parametric Statistical Tests
Parametric Statistical Tests for Different Samples
Parametric Statistical Measures for Calculating the Difference Between Means
Significance of Difference Between the Means of Two Independent Large and
Small Samples
Significance of the Difference Between the Means of Two Dependent Samples
Significance of the Difference Between the Means of Three or More Samples
Parametric Statistics Measures Related to Pearson’s ‘r’
Non-parametric Tests Used for Inference
A hypothesis is a testable statement about the relationship between two or more variables and errors reveal about the rejection and acceptance of the statement.
Please like, comment and share
1. Illustrate:
Null hypothesis
Alternative hypothesis
Level of significance
Rejection region; and
Types of error in hypothesis testing
2. Calculate the probabilities of commanding a Type I and Type II error.
Visit the website for more Services it can offer: https://cristinamontenegro92.wixsite.com/onevs
The FBI routinely uses a lie detector test to determine if a person .pdfamrahlifestyle
The FBI routinely uses a lie detector test to determine if a person is making truthful statements or
not. A person is presumed to be truthful unless the lie detector machine shows evidence of
dishonesty.
a. Specify the null and alternative hypotheses in this particular application. (2)
b. Explain what a type I and a type II error would be in this particular application.(2)
The FBI routinely uses a lie detector test to determine if a person is making truthful statements
or not. A person is presumed to be truthful unless the lie detector machine shows evidence of
dishonesty. Specify the null and alternative hypotheses in this particular application. Explain
what a type I and a type II error would be in this particular application.
Solution
(a) Null hypothesis: a person is making truthful statements
Alternative hypothesis: a person is not making truthful statements
(b) Type I error: Reject the null hypothesis when it is true.
When a person is making truthful statements, we reject it.
Type II error: Do not reject the null hypothesis when it is false.
When a person is not making truthful statements, we accept it..
This presentation accompanies a Malayalam video on writing literature reviews in Social Sciences.
The video can be found at https://www.youtube.com/c/DrChinchuC
Research ethics in behavioural sciences 05 01 2022Dr. Chinchu C
This is a presentation on Research Ethics in Behavioural Sciences, presented as a part of 18 days FDP on Research Methods in Behavioural Sciences, conducted by ASCENT
This presentation covers the basics of preparing a research proposal in Social Sciences.
A Malayalam video explaining this presentation can be accessed at https://youtu.be/acg9Y3mQs9A
Note: This is not suitable for preparing a proposal for research funding
Identifying journals for publication youtubeDr. Chinchu C
The presentation is about how to be careful while selecting academic journals for publication.
Malayalam YouTube video based on this presentation is available at https://youtu.be/z5_LD7qqzbw
Content:
When to start searching for journals
General and Specialized Journals
Acceptance Rates
Journal Selection Tools
Journal Indexing
Web of Science
Scopus
Medline, PubMed, and PubMed Central
UGC CARE
Journal Metrics
Impact Factor
CiteScore
Checklist for Journal Selection
Predatory Journals
Cloned/Hijacked Journals
Some Useful Places
Jamovi is a free and open source statistical data analysis software, built upon 'R'.
It is an easy-to-use alternative to proprietary data analysis software and is a community driven project, with new features being added regularly.
This presentation is about shortlisting and choosing journals for publishing. It also discusses quality issues, including predatory and hijacked journals. Most appropriate for Social Science students.
Structured observation as research_methodDr. Chinchu C
Structured observation is a predominantly quantitative tool. Here it is being introduced as a standalone research method in Qualitative Psychological Research
A guide to preparing Research Reports/Dissertations in Qualitative Psychology. The Structure, format and features of a report are underlined. Simple language
Qualitative methods in Psychology ResearchDr. Chinchu C
An introduction to Qualitative Methods in Psychology. Intended mostly for UG/PG students. Conveys the essentials of Ontology and Epistemology and moves on to the popular methods in Qualitative Psychological Research
Interviewing in qualitative psychology researchDr. Chinchu C
Interview is probably the most widely used tool in Qualitative Psychological Research. A step-by-step guide to conducting Interviews for Qualitative Research in Psychology is given. An Introduction to Interview Data Analysis is also provided. The Do's and Don'ts of Interviewing are outlined in detail.
Focus group discussions in psychological researchDr. Chinchu C
Explains the Why, How and When of Focus Group Discussion as a Research Method in Qualitative Psychological Research. A Practical guide with necessary points to be remembered
Familiarizing Case Study as a Research Method in Qualitative Psychological Research. Design, Conduction and Reporting are Covered. A step-by-step guide with a Philosophical justification
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...
Type I and Type II Errors in Research Methodology
1. Type I and Type II Errors
Explained in Malayalam
Dr. Chinchu C.
2. Type I Error
• A Type I error is when we reject the null hypothesis when it
is in fact true
• In other words, it is when we wrongly assume that there is
an effect when no such effect really exists
• Like a False Positive result in a Covid-19 RTPCR test
3. Type I Error Example
• Assume that we are testing to see if there is a statistically significant
difference between the average marks obtained by female and
transgender students in a class
• Also assume that in reality, there is no such significant difference
• The Null Hypothesis will be “Average Marks of Females and Average
Marks of Transgender persons are not statistically different”
• H0: Marks(Female) = Marks(TG)
• Rejecting this Null Hypothesis would mean that we conclude that
there is a significant difference in the average marks between female
and transgender students.
• A Type I error is when we wrongly conclude that there is such a
significant difference when no such significant difference actually
exists.
4. Type II Error
• A Type II error is when we do not reject the null hypothesis
when in fact we should have rejected it
• In other words, it is when we conclude that there is no
effect, when in reality there is an effect
• Like a false negative in Covid-19 RTPCR test
5. Type II Error Example
• Assume that we are testing to see if there is a statistically significant
difference between the Average BMI of Active persons and Sedentary
persons
• Also assume that in reality, there exists such a significant difference
• The Null Hypothesis will be “BMI of Active persons and BMI of
Sedentary persons are not statistically different”
• H0: BMI(Active) = BMI(Sedentary)
• Failing to Reject the Null Hypothesis would mean that we conclude that
there is a no significant difference in the average BMI between Active
and Sedentary persons.
• A Type II error is when we wrongly conclude that there is no significant
difference when a difference actually exists.
6. Type I error is considered more serious in Research
(and the one that we try to control the most)
7. 1000 culprits can escape, but, one innocent person
should not be punished
ആയിരം കുറ്റവാളികൾ രക്ഷപ്പെട്ടാലും ഒരു
നിരപരാധി പപാലും ശിക്ഷിക്കപ്പെടരുത
8. നിരപരാധി
Innocent
(Null Hypothesis True)
Type I Error
Type II Error
കുറ്റവാളി
Guilty
(Null Hypothesis False)
ശിക്ഷിക്കുന്നു
Guilty Verdict
(Rejecting Null Hypothesis)
പ്പവറുപ്പെ വിടുന്നു
Not Guilty Verdict
(Failing to Reject Null
Hypothesis)
Our Decision
9. The 𝛂 (Level of Significance)
• The level of significance is commonly understood as the probability of
making a Type I error.
• Typically set at 5% (expressed as 0.05) to balance between the
probability for Type I and Type II errors.