INFERENTIAL STATISTICS: AN INTRODUCTIONJohn Labrador
For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study.
Systematic sampling in probability sampling Sachin H
This is a systematic sample in probability sampling which is consider to be one of the technics of sampling . It is most useful in certain circumstances in Random sampling.
INFERENTIAL STATISTICS: AN INTRODUCTIONJohn Labrador
For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study.
Systematic sampling in probability sampling Sachin H
This is a systematic sample in probability sampling which is consider to be one of the technics of sampling . It is most useful in certain circumstances in Random sampling.
Probability Sampling Method- Concept - Types Sundar B N
This ppt contains Probability Sampling Method- Concept - Types which also covers Types of Sampling
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
Reasons for Sampling
and advantages and disadvantages of each methods
Explains use of statistical power, inferential decision making, effect sizes, confidence intervals in applied social science research, and addresses the issue of publication bias and academic integrity.
Probability Sampling Method- Concept - Types Sundar B N
This ppt contains Probability Sampling Method- Concept - Types which also covers Types of Sampling
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
Reasons for Sampling
and advantages and disadvantages of each methods
Explains use of statistical power, inferential decision making, effect sizes, confidence intervals in applied social science research, and addresses the issue of publication bias and academic integrity.
Data Collection tools: Questionnaire vs ScheduleAmit Uraon
Questionnaire is one of the important method of data collection in which a researcher distributes a questionnaire to the respondents and requests them to fill up the questionnaire and return.
Same way Schedule is also a set of structured questions and the answers in questionnaire is not filled up by respondents themselves but by enumerators.
In classical sampling theory, why is a model always developed on a sample even when the whole data is available? Because all the observations and variables may not be needed for developing a model, because they are not relevant for the development.
Sampling and Inference: Learn about the importance of random sampling in political research; learn why samples that seem small can yield accurate information about larger groups; learn how to figure out the margin of error of a sample; learn how to make inferences about the information in a sample.
Basics of Educational Statistics (Inferential statistics)HennaAnsari
Inferential Statistics
6.1 Introduction to Inferential Statistics
6.1.1 Areas of Inferential Statistics
6.2.2 Logic of Inferential Statistics
6.2 Importance of Inferential Statistics in Research
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.
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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.
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The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
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Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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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.
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.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
2. WHAT IS INFERENTIAL STATISTICS?
Inferential statistics is a technique used to draw
conclusions about a population by testing the data
taken from the sample of that population.
It is the process of how generalization from sample to
population can be made. It is assumed that the
characteristics of a sample is similar to the population’s
characteristics.
It includes testing hypothesis and deriving estimates.
It focuses on making statements about the population.
Statisticsconsultation.co
3. THE PROCESS OF INFERENTIAL ANALYSIS
Raw Data
• It comprises of all the data collected from the sample.
• Depending on the sample size, this data can be large or small
set of measurements.
Sample
Statistics
• It summarizes the raw data gathered from the sample of
population
• These are the descriptive statistics (e.g. measures of central
tendency)
Inferential
Statistics
• These statistics then generate conclusions about the
population based on the sample statistics.
Statisticsconsultation.co
4. SAMPLING METHODS
Random sampling is the best type of sampling method
to use with inferential statistics. It is also referred to as
probability sampling.
In this method, each participant has an equal
probability of being selected in the sample.
In case the population is small enough then everyone
can be used as a participant.
Another sampling technique is Snowball sampling
which is a non-probability sampling.
Snowball sampling involves selecting participants on
the basis of information provided by previously studied
cases. This technique is not applied for inferential
statistics.
Statisticsconsultation.co
5. IMPORTANT DEFINITIONS
Probability is the mathematical possibility that a
certain event will take place. They can range from 0 to
1.00
Parameters describe the characteristics of a sample of
population. (Variables such as age, gender, income,
etc.).
Statistics describe the characteristics of a sample on
the same types of variables.
Sampling Distribution is used to make inferences
based on the assumption of random sampling.
Statisticsconsultation.co
6. SAMPLING ERROR CONCEPTS
Sampling Error: Inferential statistics takes sampling
error (random error) into account. It is the degree to
which a sample differs on a key variable from the
population.
Confidence Level:
The number of times out of 100 that the true value will
fall within the confidence interval.
Confidence Interval:
A calculated range for the true value, based on the
relative sizes of the sample and the population.
Sampling error describes the difference between
sample statistics and population parameters.
Statisticsconsultation.co
7. SAMPLING DISTRIBUTION CONCEPTS
The variables of a
sample taken
from the
population
should be the
same for the
population also.
Due to sampling
error, the sample
mean can be
varied.
The amount of
this variation in
the sample mean
is referred to as
standard error.
Standard error
decreases as the
sample size
increases.
Statisticsconsultation.co
8. TYPES OF HYPOTHESES
Alternative hypothesis: It specifies expected
relationship between two or more variables. It may be
symbolized by H1 or Ha.
Null hypothesis: It is the statement that says there is
no real relationship between the variables described in
the alternative hypothesis.
In inferential statistics, the hypothesis that is actually
tested is the null hypothesis. Therefore, it is essential to
prove that the null hypothesis is not valid and
alternative hypothesis is true and should be accepted.
Statisticsconsultation.co
9. HYPOTHESIS TESTING PROCESS
State the research
hypothesis
State the null
hypothesis
Choose a level of
statistical
significance
Select and
compute the test
statistic
Make a decision
regarding whether
to accept or reject
the null hypothesis.
Statisticsconsultation.co