The number that divides the normal distribution into region where we will reject the null hypothesis and the region where we fail to reject the null hypothesis. For normal distribution Z at 5% level of significance (z= plus-minus 1.96) is often referred to as the critical value (or critical region).
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 number that divides the normal distribution into region where we will reject the null hypothesis and the region where we fail to reject the null hypothesis. For normal distribution Z at 5% level of significance (z= plus-minus 1.96) is often referred to as the critical value (or critical region).
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
Hypothesis testing and estimation are used to reach conclusions about a population by examining a sample of that population.
Hypothesis testing is widely used in medicine, dentistry, health care, biology and other fields as a means to draw conclusions about the nature of populations
Following points are presented in this presentation.
1. Hypothesis testing is a decision-making process for evaluating claims about a population.
2. NULL HYPOTHESIS & ALTERNATIVE HYPOTHESIS.
3. Types of errors.
A hypothesis is the translation of the information that we are keen on. Utilizing Hypothesis Testing, we attempt to decipher or reach inferences about the populace utilizing test information. A Hypothesis assesses two totally unrelated articulations about a populace to figure out which explanation is best upheld by the example information.
No research is done in a void: science is constantly expanding previous hypotheses, building upon past knowledge. We live in a digital age where information is ubiquitous, yet we struggle to preserve accurate machine readable and quantitative descriptions of our research compromising our capacity to use them in our inferences. In the following talk I will show how and why we incorporate assumptions in our studies based on three experiments we have conducted: (i) dissociating metacognitive subdomains in medial and lateral anterior prefrontal cortex, (ii) relating reading comprehension to individual differences in the default mode network, and (iii) exploring neural correlates of the content and form of self-generated thoughts. This will be followed by introducing a new inference method - probabilistic Regions of Interest (pROI) - which allows the use of prior knowledge in the form of a probabilistic map. This approach provides the middle ground between ROI and full brain analysis, by giving researchers more flexibility in formalizing priors. The quality of prior probability maps based on the literature can be improved by using unthresholded statistical maps instead of peak coordinates. To facilitate this we have created NeuroVault.org - a community - wide effort to collect unthresholded statistical maps. Taking the initiative a step further I will describe the concept of data papers - publications purely dedicated to datasets. Together those three mechanisms (pROI, NeuroVault.org and data papers) are a small but significant steps towards better, more reusable and reproducible science.
Hypothesis testing and estimation are used to reach conclusions about a population by examining a sample of that population.
Hypothesis testing is widely used in medicine, dentistry, health care, biology and other fields as a means to draw conclusions about the nature of populations
Following points are presented in this presentation.
1. Hypothesis testing is a decision-making process for evaluating claims about a population.
2. NULL HYPOTHESIS & ALTERNATIVE HYPOTHESIS.
3. Types of errors.
A hypothesis is the translation of the information that we are keen on. Utilizing Hypothesis Testing, we attempt to decipher or reach inferences about the populace utilizing test information. A Hypothesis assesses two totally unrelated articulations about a populace to figure out which explanation is best upheld by the example information.
No research is done in a void: science is constantly expanding previous hypotheses, building upon past knowledge. We live in a digital age where information is ubiquitous, yet we struggle to preserve accurate machine readable and quantitative descriptions of our research compromising our capacity to use them in our inferences. In the following talk I will show how and why we incorporate assumptions in our studies based on three experiments we have conducted: (i) dissociating metacognitive subdomains in medial and lateral anterior prefrontal cortex, (ii) relating reading comprehension to individual differences in the default mode network, and (iii) exploring neural correlates of the content and form of self-generated thoughts. This will be followed by introducing a new inference method - probabilistic Regions of Interest (pROI) - which allows the use of prior knowledge in the form of a probabilistic map. This approach provides the middle ground between ROI and full brain analysis, by giving researchers more flexibility in formalizing priors. The quality of prior probability maps based on the literature can be improved by using unthresholded statistical maps instead of peak coordinates. To facilitate this we have created NeuroVault.org - a community - wide effort to collect unthresholded statistical maps. Taking the initiative a step further I will describe the concept of data papers - publications purely dedicated to datasets. Together those three mechanisms (pROI, NeuroVault.org and data papers) are a small but significant steps towards better, more reusable and reproducible science.
How to hack your brain for effortless learningpebble {code}
Peter talks about how folks are currently hacking their brain to learn new skills more quickly.
In this talk, Peter will talk about:
How your brain works
Ways to hack your brain
Using virtual reality as a training tool
During Expo Day selected Summit Sponsors will showcase their latest initiatives and solutions:
-- Preview the Future of Brain Health with Anu Acharya, Founder and CEO of Map My Genome
-- The Alzheimer’s Research and Prevention Foundation (ARPF): Discuss new science and prevention initiatives with President Dr. Dharma Singh Khalsa.
-- FitBrains (a Rosetta Stone company): Explore ongoing big data research with Conny Lin, Data Research Scientist & Policy Analyst.
Presentation @ The 2015 SharpBrains Virtual Summit http://sharpbrains.com/summit-2015/agenda
On the large scale of studying dynamics with MEG: Lessons learned from the Hu...Robert Oostenveld
As part of the Human Connectome Project (HCP), which includes high-quality fMRI, anatomical MRI, DTi and genetic data from 1200 subjects, we have scanned and investigated a subset of 100 subjects (mostly comprised of pairs of twins) using MEG. The raw data acquired in the HCP has been analyzed using standard pipelines [ref1] and both raw and results at various levels of processing have been shared though the ConnectomeDB [ref2].
Throughout the process of the HCP we have not only analyzed (resting state) MEG data, but also have developed the data analysis protocols, the software and the strategies to achieve reproducible MEG connectivity results. The MEG data analysis software is based on FieldTrip, an open source toolbox [ref3], and is shared alongside the data to allow the analyses to be repeated on independent data.
In this presentation I will outline what the HCP MEG team has learned along the way and I will provide recommendations on what to do and what to avoid in making MEG studies on (resting state) connectivity more reproducible.
1. Larson-Prior LJ, Oostenveld R, Della Penna S, Michalareas G, Prior F, Babajani-Feremi A, Schoffelen JM, Marzetti L, de Pasquale F, Di Pompeo F, Stout J, Woolrich M, Luo Q, Bucholz R, Fries P, Pizzella V, Romani GL, Corbetta M, Snyder AZ; WU-Minn HCP Consortium. Adding dynamics to the Human Connectome Project with MEG. Neuroimage, 2013.
doi:10.1016/j.neuroimage.2013.05.056
2. Hodge MR, Horton W, Brown T, Herrick R, Olsen T, Hileman ME, McKay M, Archie KA, Cler E, Harms MP, Burgess GC, Glasser MF, Elam JS, Curtiss SW, Barch DM, Oostenveld R, Larson-Prior LJ, Ugurbil K, Van Essen DC, Marcus DS. ConnectomeDB-Sharing human brain connectivity data. Neuroimage, 2016. doi:10.1016/j.neuroimage.2015.04.046
3. Oostenveld R, Fries P, Maris E, Schoffelen JM. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Comput Intell Neurosci. 2011. doi:10.1155/2011/156869
We're curious minds, hackers and tinkerers.
We love to tweak our tools.
But our most important and wonderful tool is our own brain.
How can we understand what's going on so we can hack it?
This is the full-text slide deck for "Hack Your Brain".
You can find all the trivia in "Hack Your Brain - Trivia" and a french (lighter) version in "Hack Your Brain - FR".
This is a lecture that I gave to a Principles of Epidemiology MPH class. It takes a critical look at the use of p-values to judge the strength of evidence, and offers more holistic, informative approaches to interpreting statistical findings such as measures of effect size and confidence intervals.
What should we expect from reproducibiliryStephen Senn
Is there really a reproducibility crisis and if so are P-values to blame? Choose any statistic you like and carry out two identical independent studies and report this statistic for each. In advance of collecting any data, you ought to expect that it is just as likely that statistic 1 will be smaller than statistic 2 as vice versa. Once you have seen statistic 1, things are not so simple but if they are not so simple, it is that you have other information in some form. However, it is at least instructive that you need to be careful in jumping to conclusions about what to expect from reproducibility. Furthermore, the forecasts of good Bayesians ought to obey a Martingale property. On average you should be in the future where you are now but, of course, your inferential random walk may lead to some peregrination before it homes in on “the truth”. But you certainly can’t generally expect that a probability will get smaller as you continue. P-values, like other statistics are a position not a movement. Although often claimed, there is no such things as a trend towards significance.
Using these and other philosophical considerations I shall try and establish what it is we want from reproducibility. I shall conclude that we statisticians should probably be paying more attention to checking that standard errors are being calculated appropriately and rather less to inferential framework.
Replication Crises and the Statistics Wars: Hidden Controversiesjemille6
D. Mayo presentation at the X-Phil conference on "Reproducibility and Replicabililty in Psychology and Experimental Philosophy", University College London (June 14, 2018)
Similar to If you liked it you should've put a p-value on it ...or not (20)
Presentation given at Organization for Human Brain Mapping Annual Meeting in Singapore 2018
Video recording: https://www.pathlms.com/ohbm/courses/8246/sections/12538/video_presentations/116214
Evaluation of full brain parcellation schemes using the NeuroVault database o...Krzysztof Gorgolewski
Slides from a talk given at SfN 2016.
The task of dividing the human brain into regions has been captivating scientists for many years. In the following work we revisit this challenge and introduce a new evaluation technique that works for both cortical and subcortical parcellations. Our approach is based on data from a diverse set of cognitive experiments that employs nonparametric methods to account for smoothness and parcel size biases.
As reported before parcel variance was a function of parcel size in that smaller parcels were more likely to be homogenous (even in random data). However, when we used map-specific null distributions to account for both smoothness of statistical maps as well as number of parcels in atlases, unbiased estimates become apparent. Both Yeo et al. and Collins et al. parcellations produce scores for random data similar to those derived from real data. In contrast, Shen et al., AAL, and Gordon et al. show lower within parcel variance when applied to real data than when applied to random data (but no distinction can be made between them).
In addition to looking at within parcel variance we also applied a novel metric based on the intuition that different parts of the brain should not only be homogenous, but also different from each other. To quantify this we calculated a ratio of between and within parcel variances (standardized using individual null models). This approach indirectly penalizes parcellations with too many unnecessary parcels. Using this measure we show that Yeo et al. parcellation fits data better (Figure 1) than Collins et al. atlas despite having fewer parcels (7 vs 10).
We present a novel approach to evaluating atlases and parcellations of the human brain that captures diverse patterns observed across many cognitive studies. Our testing methodology overcomes biases introduced by the size of the parcels and smoothness of input data, but also, in contrast to previous methods, can be applied to whole brain volumetric data. We have found that in contrast to previous reports based on resting state cortico cortical connectivity Shen et al. and AAL atlases can delineate brain regions with above average accuracy.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
13. Hypothesis testing
• Distinguish between two hypotheses
1. H0 – there is no difference between groups
2. H1 – there is a difference between groups
• Or…
1. H0 – there is no relation between two variables
2. H1 – there is some relation between the two
variables
14. From statistical values to p-values
• Various procedures give us statistical values
– T-tests (one sample, two sample, paired etc.)
– F-Tests
– Correlation tests (r values)
• What is a p value?
15. P value
• P(z) = A probability if we repeat our
experiment (with all the analyses) and there is
no effect we will get this or greater statistical
value.
17. OK back to neuroimaging
• Assuming that we are doing a massive
univariate analysis (we look at each voxel
independently) we have a t-map
• Now using a theoretical distribution (given the
degrees of freedom) we can turn it into a p-
map
18. Inference!
• We take out p-map discard all voxel with
values > 0.05
– “The value for which P=0.05, or 1 in 20, is 1.96 or
nearly 2; it is convenient to take this point as a
limit in judging whether a deviation ought to be
considered significant or not. Deviations
exceeding twice the standard deviation are thus
formally regarded as significant.”
• We are done – right?
19. Not quite done yet…
• Let me generate two vectors of values and test
using a t-test if they are different
• What is the probability that P(t) < 0.05
– Well… 0.05
• Let me generate another set of values… and
another… 100 pairs of vectors
• What is the probability that at least one of the
test?
21. Correcting for multiple comparisons
• Bonferroni correction (based on Bool’s
inequality)
– Divide your p-threshold by the number of tests
you have performed
– Or multiple your p-values by the number of tests
you have performed
22. Bonferroni is a Family Wise Error
correction
It guarantees that the chances of getting at least
one false positive in all the tests is less than your
p-threshold
23. Permutation based FWE correction
• The assumptions behind the theoretical
distributions are often not met
• There are many dependencies between voxels
– Each test is not independent so Bonferroni
correction can be conservative
• We can however establish an empirical
distribution
24. Permutation based FWE correction
1. Break the relation: shuffle the participants
between the groups
2. Perform the test
3. Save the maximum statistical value across
voxels
4. Repeat
25. Permutation based FWE correction
Our FWE corrected p value is the percentage of
permutations that yielded statistical values
higher than the original (unshuffled one)
26. False Discovery Rate
• Even conceptually FWE correction seems
conservative
– At least one test out of 60 000?
• Is there a more intuitive way of looking at
this?
27. False Discovery Rate
I present a number of voxels that I think show a
strong effect, but I admit that a certain
percentage of them might be false positives.
29. FDR procedures
• Benjamini-Hochberg procedure
– With it’s dependent variables variant
• Efrons local FDR procedure
– Explicit modeling of the signal distribution
30. Interim Summary
• FWE corrections
– Bonferroni – simple but struggles with
dependencies (over conservative)
– Permutations – less dependent on assumptions,
but time consuming
• FDR corrections
– B-H – simple but also struggles with dependencies
– Local FDR – data driven, but can fail in case of low
SNR
31. CLUSTER EXTENT TESTS
Test how big are the blobs
Random field theory
Smoothness estimation
Permutation test
The problem of cluster forming threshold
Fun fact: FWE with RFT
32. Intuition
If we are interested in continuous regions of
activations why are we looking at voxels not
blobs?
35. What contributes to expected cluster
size?
How likely is to get cluster of this size from pure
noise?
It depends… on:
1. cluster forming threshold
2. smoothness of the map
3. size of the map
36. Where do we get those parameters?
1. cluster forming threshold
– Arbitrary decision
2. smoothness of the map
– Estimated from the residuals of the GLM
3. size of the map
– Calculated from the mask
37. Permutation based cluster extent
probability
1. Break the relation: shuffle the participants
between the groups
2. Perform the test
3. Threshold the map to get clusters
4. Save the sizes of all clusters
5. Repeat
38. Permutation based cluster extent
probability
Our cluster extent p value is the percentage of
permutations that yielded cluster sizes bigger
than the original (unshuffled one)
53. P-value paradox
• There are no two entities or groups that are
truly identical
• There are no two variables that are in no way
unrelated
• We just fail to obtain enough samples to see it
– Or our tools are not sensitive enough
54. More samples more “significance”
• The more subjects you will have in your study
the more likely it is that you will find
something significant
• The same applies to scan length, and field
strength
56. P-value failure
• P-values do not tell us much about actual size
of the effect
• Neither do they tell of the predictive power of
the found relation
57. The interesting question
Is PCC involved in autism?
vs.
Given cortical thickness of a subjects PCC how
well am I able to predict his or hers diagnosis?
58. Why does this matter
• More subjects, longer scans, stronger scans –
everything is significant
– We are getting there
• Lack of faith in science from the public
– Poor reproducibility
59. What needs to be done
We need more replications
We need to start reporting null results
60. What you can do
• Report effect sizes and their confidence
intervals
– For all test/voxels – not just those significant
• Share the unthresholded statistical maps
– It only takes 5 minutes on neurovault.org
• Report all the tests you have performed – not
just the significant ones