The process of selecting a number of individuals for a study in such a way that the individuals represent the larger group from which they were selected
The process of selecting a number of individuals for a study in such a way that the individuals represent the larger group from which they were selected
Stratified Sampling and Cluster Sampling that are most commonly contrasted by the people. There is a big difference between stratified and cluster sampling, which in the first sampling technique, the sample is created out of the random selection of elements from all the strata while in the second method, all the units of the randomly selected clusters form a sample. Just have a look for better understanding.
Stratified Sampling and Cluster Sampling that are most commonly contrasted by the people. There is a big difference between stratified and cluster sampling, which in the first sampling technique, the sample is created out of the random selection of elements from all the strata while in the second method, all the units of the randomly selected clusters form a sample. Just have a look for better understanding.
Aqui estão algumas dicas para aumentar o rendimento de estudantes em seus estudos. Compreendendo como o cérebro funciona e utilizando-se de técnicas científicas, pode-se aprender mais facilmente
Detailed Lesson Plan (ENGLISH, MATH, SCIENCE, FILIPINO)Junnie Salud
Thanks everybody! The lesson plans presented were actually outdated and can still be improved. I was also a college student when I did these. There were minor errors but the important thing is, the structure and flow of activities (for an hour-long class) are included here. I appreciate all of your comments! Please like my fan page on facebook search for JUNNIE SALUD.
*The detailed LP for English is from Ms. Juliana Patricia Tenzasas. I just revised it a little.
For questions about education-related matters, you can directly email me at mr_junniesalud@yahoo.com
SAMPLING METHODS ( PROBABILITY SAMPLING).pptxPoojaSen20
SAMPLING
SAMPLING IS THE PROCESS OF SELECTING A SMALL NUMBER OF ELEMNTS FROM A LARGER DEFINED TARGET GROUP OF ELEMNTS SUCH THAT THE INFORMATION GATHERDED FROM THE SMALL GROUP WILL ALLOW JUDEN=MENT TO BE MADE ABOUT THE LARGER GROUPS.
IN SIMPLE WORDS A PROCEDURE BY WHICH SOME MEMBERS OF A GIVEN POPULATION ARE SELECTED AS REPRESENTATION OF THE ENTIRE POPULATION .
PURPOSE OF SAMPLING
To gather data about the population in order to make an inference that can be generalized to the populations. .
PROBABILITY SAMPLING
Probability sampling is a type of sampling where each member of the population has a known probability of being selected in the sample .
In probability sampling some elements of randomness is involved in selection of units ,so that personal judgement or bias is not there.
NON- PROBABILITY SAMPLING
Non- Probability sampling is a type of sampling where each member of the population does not have known probability of being selected in the sample.
In this each member of the population does not get equal chance of being selected in the sample.
This sampling methods is adopted when each member of the population can not be selected or the researcher deliberately wants to choose member selectively
What is Population ?
What is Sample ?
Sampling Techniques
What is Probability sampling ?
What is Non-probability sampling ?
Advantages & Disadvantages sampling
Difference b/w Probability &Non-Probability
Characteristics of sampling
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
2. DEFINITION: SAMPLING
The process of selecting a number of
individuals for a study in such a way
that the individuals represent the
larger group from which they were
selected.
4. TARGET VERSUS
ACCESSIBLE POPULATIONS:
1.
The Target Population is the ideal selection of actual
population which researcher really like to generalize:
- is rarely available.
- Researcher’s ideal choice.
2. The Accessible or ‘available’ population is the
population to which a researcher is able to generalize:
- Researcher’s realistic selection
7. SIMPLE RANDOM SAMPLING
The proces of selecting a sample that
allows induvidual in the defined
population to have an equal and
independent chance of being selected
for the sample.
8. STEPS IN RANDOM SAMPLING:
1. Identify and define the population.
2. Determine the desired sample size.
3. List all members of the population.
4. Assign all individuals on the list consecutive
number from zero to the required number.
Each individual must have the same number
of digits as each other individual.
9. STEPS IN RANDOM SAMPLING:
5.
Select an arbitrary number in the table of
random numbers.
6. For the selected number, look only at the
number of digits assigned to each population
member.
10. STEPS IN RANDOM SAMPLING:
7.
8.
If the number corresponds to the number
assigned to any of the individual in the
population, then that individual is included
in the sample.
Go to the next number in the column and
repeat step #7 until the desired number of
individuals has been selected for the
sample.
11. ADVANTAGES OF SIMPLE
RANDOM SAMPLING:
Easy to conduct
Strategy requires minimum
knowledge of the population to be
sampled
12. DISADVATAGES OF SIMPLE
RANDOM SAMPLING:
Need names of all population members.
May over-represent or under-estimate
sample members.
There is difficulty in reaching all selected
in the sample.
13. STRATIFIED RANDOM
SAMPLING
The process of selecting a sample
that allows identified subgroups in
the defined population to be
represented in the same proportion
that they exist in the population.
14. STEPS IN STRATIFIED
SAMPLING:
1. Identify and define the population.
2. Determine the desired sample size.
3. Identify the variable and subgroups (strata)
for which you want to guarantee appropriate,
equal representation.
15. STEPS IN STRATIFIED RANDOM
SAMPLING
4.
Classify all members of the population as
members of the one identified subgroup.
5.
Randomly select, using a table of random
numbers; an “appropriate” number of
individuals from each of the subgroups,
appropriate meaning an equal number of
individuals.
16. ADVANTAGES OF STRATIFIED
RANDOM SAMPLING:
More precise sample.
Can be used both proportions and
stratification sampling.
Sample represents the desired strta.
18. CLUSTER SAMPLING
The process of randomly selecting
intact groups, not individuals, within
the defined population sharing
similar characteristics.
19. STEPS IN CLUSTER RANDOM
SAMPLING:
1. Identify and define the population.
2. Determine the desired sample size.
3. Identify and define a logical cluster.
20. STEPS IN CLUSTER RANDOM
SAMPLING:
4. List all clusters (or obtain a list) that make up
the population of clusters.
5. Estimate the average number of population
members per cluster.
6. Determine the number of clusters needed by
dividing the sample size by the estimated
size of a cluster.
21. STEPS IN CLUSTER RANDOM
SAMPLING:
7. Randomly select the needed number of
clusters by using a table of random
numbers.
8. Include in your study all population
members in each selected cluster.
22. ADVANTAGES OF CLUSTER
RANDOM SAMPLING:
Efficient.
Researcher does not need nemes of
all population members.
Reduces travel to site.
Useful for educational research.
28. SYSTEMATIC SAMPLING
The process of selecting individuals
within the defined population from a
list by taking every Kth name.
29. STEPS IN SYSTEMATIC
SAMPLING:
1. Identify and define the population.
2. Determine the desired sample size.
3. Obtain a list of the population.
4. Determine what K is equal to by dividing the size of
the population by the desired sample size.
30. STEPS IN SYSTEMATIC
SAMPLING:
5. Start at some random place in the population
list. Close your eyes and point your finger to
a name.
6. Starting at that point, take every Kth name on
the list until the desired sample size is
reached.
7. If the end of the list is reached before the
desired sample is reached, go back to the top
of the list.
32. DISADVANTAGES OF
SYSTEMATIC SAMPLING:
All members of the population do not
have an equal chance of being selected.
The Kth person may be related to a
periodical order in the population list,
producing unrepresentativeness in the
sample.
33. CONVENIENCE SAMPLING
The process of including whoever
happens to be available at the time .
It is also called “accidental” or
“haphazard” sampling.
35. PURPOSIVE SAMPLING
The process whereby the researcher
selects a sample based on experience
or knowledge of the group to be
sampled. It is also called “judgment”
sampling.