How many people do I need to survey? How many is too many. What are the costs v the benefits. Determining sample size --- the correct sample--- is the foundation for great surveys and part of your overall market research strategy.
This presentation will address the issue of sample size determination for social sciences. A simple example is provided for every to understand and explain the sample size determination.
How many people do I need to survey? How many is too many. What are the costs v the benefits. Determining sample size --- the correct sample--- is the foundation for great surveys and part of your overall market research strategy.
This presentation will address the issue of sample size determination for social sciences. A simple example is provided for every to understand and explain the sample size determination.
Minimizing Risk In Phase II and III Sample Size CalculationnQuery
[ Watch Webinar: http://bit.ly/2thIgmi ]. In this free webinar, Head of Statistics at Statsols, Ronan Fitzpatrick, addresses the issues of reducing risk in Phase II/III sample size calculations. Topics covered will include:
Sample Size Determination For Different Trial Designs
Bayesian Sample Size Determination
Sample Size For Survival Analysis
& more
5 essential steps for sample size determination in clinical trials slidesharenQuery
In this free webinar hosted by nQuery Researcher & Statistician Eimear Keyes, we map out the 5 essential steps for sample size determination in clinical trials. At each step, Eimear will highlight the important function it plays and how to avoid the errors that will negatively impact your sample size determination and therefore your study.
Watch the Video: https://www.statsols.com/webinar/the-5-essential-steps-for-sample-size-determination
Sample size calculation in medical researchKannan Iyanar
A short description on estimation of sample size in health care research. It describes the basic concepts in sample size estimation and various important formulae used for it.
Minimizing Risk In Phase II and III Sample Size CalculationnQuery
[ Watch Webinar: http://bit.ly/2thIgmi ]. In this free webinar, Head of Statistics at Statsols, Ronan Fitzpatrick, addresses the issues of reducing risk in Phase II/III sample size calculations. Topics covered will include:
Sample Size Determination For Different Trial Designs
Bayesian Sample Size Determination
Sample Size For Survival Analysis
& more
5 essential steps for sample size determination in clinical trials slidesharenQuery
In this free webinar hosted by nQuery Researcher & Statistician Eimear Keyes, we map out the 5 essential steps for sample size determination in clinical trials. At each step, Eimear will highlight the important function it plays and how to avoid the errors that will negatively impact your sample size determination and therefore your study.
Watch the Video: https://www.statsols.com/webinar/the-5-essential-steps-for-sample-size-determination
Sample size calculation in medical researchKannan Iyanar
A short description on estimation of sample size in health care research. It describes the basic concepts in sample size estimation and various important formulae used for it.
Lecture to Master of Business Management Students (MBM) at the Moshi Cooperative University, Moshi Tanzania. The Objective was that at the end of the lecture students should be able to determine sample size scientifically.
Are you looking to expand your research toolkit to include some quantitative methods, such as survey research or A/B testing? Have you been asked to collect some usability metrics, but aren’t sure how best to go about that? Or do you just want to be more aware of all of the UX research possibilities? If your answer to any of those questions is yes, then this session is for you.
You may know that without statistics, you won’t know if A is really better than B, if users are truly more satisfied with your new site than with your old one, or which changes to your site have actually impacted conversion rates. However, statistics can also help you figure out how to report satisfaction and other metrics you collect during usability tests. And they’re essential for making sense of the results of quantitative usability tests.
This session will focus on the statistical concepts that are most useful for UX researchers. It won’t make you a quant, but it will give you a good grounding in quantitative methods and reporting. (For example, you will learn what a margin of error is, how to report quantitative data collected during a usability test - and how not to - and how many people you really need to fill out a survey.)
Statistics for UX Professionals - Jessica CameronUser Vision
Are you looking to expand your research toolkit to include some quantitative methods, such as survey research or A/B testing? Have you been asked to collect some usability metrics, but aren’t sure how best to go about that? Or do you just want to be more aware of all of the UX research possibilities? If your answer to any of those questions is yes, then this session is for you.
You may know that without statistics, you won’t know if A is really better than B, if users are truly more satisfied with your new site than with your old one, or which changes to your site have actually impacted conversion rates. However, statistics can also help you figure out how to report satisfaction and other metrics you collect during usability tests. And they’re essential for making sense of the results of quantitative usability tests.
This session will focus on the statistical concepts that are most useful for UX researchers. It won’t make you a quant, but it will give you a good grounding in quantitative methods and reporting. (For example, you will learn what a margin of error is, how to report quantitative data collected during a usability test - and how not to - and how many people you really need to fill out a survey.)
A sample design is a definite plan for obtaining a sample from a given population. Researcher must select/prepare a sample design which should be reliable and appropriate for his research study.
Lecture 6 Point and Interval Estimation.pptxshakirRahman10
Point and Interval Estimation:
Objectives:
Apply the basics of inferential statistics in terms of point estimation.
Compute point and estimation of population means and confidence interval.
Interpret the results of point and interval estimation.
Estimation:
Estimating the value of parameter from the sample:
An aspect of inferential statistics.
Why to estimate: Population is large enough so we
can only estimate.
Types of estimation:
Point Estimation:
A specified number value (single value) that is an estimate of a population parameter. The point estimate of the population mean µ is the sample mean.
Interval Estimate:
Range of values to estimate about population parameter.
Confidence Interval Estimation:
Range of values to estimate about population parameter.
May contain the parameter or not (Degree of confidence).
Ranges between two values.
Example:
Age (in years) 4 BScN students: 20<µ < 25 or (22.5 +2.5)
FORMULA:
Point estimate (x) + Critical Value x Standard Error.
Confidence Interval is a particular interval of estimate.
Given that sample size is large, the 95% of the sample means taken from same population and same sample size will fall in + 1.96 SD of the population mean.
Three commonly used Confidence Intervals are 90%, 95% (by default) , and 99%.
Why not too small or too large confidence intervals?
Too wide: 99.9% Interval too broad
Too narrow: 80 % More uncertainty to have population mean.
The 99% of the sample means taken from same population and
same sample size will fall in + 2.575 SD of the population mean.
Interpretation:
99% probability that interval will enclose population parameter and 1% chance that it will not have population parameter.
Level of confidence: The level of certainty that the interval will have the true population mean.
Chances of Error: Chances that the interval will not cater the true parameter.
Sum of level of confidence and chances of error =100%
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
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!
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
2. Learning Objectives
• To understand the eight axioms underlying sample size determination with a
probability sample
• To know how to compute sample size using the confidence interval approach
• To become aware of practical considerations in sample size determination
• To be able to describe different methods used to decide sample size,
including knowing whether a particular method is flawed
3.
4.
5. Key Points
• Many managers falsely believe that sample size and sample
representativeness are related, but they are not.
• A sample size decision is usually a compromise between what is theoretically
perfect and what is practically feasible.
• Many practitioners have a large sample bias, which is the false belief that
sample size determines a sample’s representativeness.
6. Important Points about Sampling
• Sampling method (not sample size) is related to representativeness.
• Only a probability sample (random sample) is truly representative of a
population.
• Sample size determines accuracy of findings.
• The only perfect accurate sample is a census - which is for the most part, not
positive in Marketing Research
7. Sample Accuracy
• Sample accuracy: refers to how close a random sample’s statistic is to the
true population’s value it represents
9. Two Types of Error
• Non sampling error: pertains to all sources of error other than sample
selection method and sample size
• Sampling error: involves sample selection and sample size
10. Sample Size and Accuracy
• Which is of these is more accurate?
• A large probability sample or
• A small probability sample?
• The larger a probability sample is, the more accurate it is (less sample
error).
15. The Confidence
Interval Method of
• Confidence interval approach:
applies the concepts of
accuracy, variability, and
confidence interval to create a
“correct” sample size
• The confidence interval
approach is based upon the
normal curve distribution.
• We can use the normal
distribution because of the
Central Limit Theorem.
16. Central Limit
Theorem
• Since 95% of samples drawn
from a population will fall within
+ or – 1.96 × sample error (this
logic is based upon our
understanding of the normal
curve), we can make the
following statement . . .
• If we conducted our study over
and over, 1,000 times, we
would expect our result to fall
within a known range. Based
upon this, we say that we are
95% confident that the true
population value falls within this
range.
18. Figuring out the Sample Error - Module 1 Handout
• n Values:
• n = 1,000
• n = 500
• n = 100
• n = 50
• p and q = 50
• Confidence Interval = 95% or 1.96
19. Figuring out the Sample Error - Module 1 Handout
• n Values:
• n = 1,000 Sample Error _____
• n = 500 Sample Error _____
• n = 100 Sample Error _____
• n = 50 Sample Error _____
• p and q = 50
• Confidence Interval = 95% or 1.96
20.
21. Sample Size Formula
• Need to know
• Variability: p × q
• Acceptable margin of sample error: e
• Level of confidence: z
23. Example: Estimating a Sample Size
• What is the required sample size?
• Five years ago, a survey showed that 42% of consumers were aware of the
company’s brand (Consumers were either “aware” or “not aware.”)
• After an intense ad campaign, management wants to conduct another survey
and they want to be 95% confident that the survey estimate will be within
±5% of the true percentage of “aware” consumers in the population.
• What is n?
24. Example: Estimating a Sample Size
• Five years ago, a survey
showed that 42% of
consumers were aware of the
company’s brand (Consumers
were either “aware” or “not
aware.”)
• After an intense ad campaign,
management wants to conduct
another survey and they want
to be 95% confident that the
survey estimate will be within
±5% of the true percentage of
“aware” consumers in the
population.
• Z=1.96 (95% confidence)
• p=42
• q=100-p=58
• e=5
• What is n?
25. Example: Estimating a Sample Size
• Five years ago, a survey
showed that 42% of
consumers were aware of the
company’s brand (Consumers
were either “aware” or “not
aware.”)
• After an intense ad campaign,
management wants to conduct
another survey and they want
to be 95% confident that the
survey estimate will be within
±5% of the true percentage of
“aware” consumers in the
population.
• Z=1.96 (95% confidence)
• p=42
• q=100-p=58
• e=5
• What is n?
• n = 374
30. Practical Considerations
• How to estimate variability (p times q) in the population?
• Expect the worst cast (p = 50; q = 50)
• Estimate variability
• Previous studies?
• Conduct a pilot study?
31. Practical Considerations
• How to determine the amount of acceptable sample error.
• Researchers should work with managers to make this decision. How much
error is the manager willing to tolerate?
• See page 251 for practical example
• Researchers should work with managers to take cost into consideration in
this decision.
32. Practical Considerations
• How to decide on the level of confidence to use.
• Researchers typically use 95% or 99%.
• Most clients would not accept a confidence interval below 95% as a
representative of the overall population
33. Other Methods of Sample Size Determination
• Arbitrary “percentage rule of thumb”
• Conventional sample size
• Statistical analysis approach requirements
• Cost basis
37. More Practice for Test Questions
• Page 258 - Question #13 - Crest Toothpaste Sample Size
• Page 247 - Sample Size Calculations Practice
• Make sure you practice and know all of the equations discussed in class
• Sample Size Margin of Error
• Sample Size Formula
• Small Population Formula