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This document discusses hypothesis testing and provides examples. It begins by explaining the typical four steps of hypothesis testing: determining the null and alternative hypotheses, collecting and summarizing data, calculating a test statistic and p-value, and making a decision. Two examples are then provided that demonstrate these steps for testing differences in means and proportions. The document concludes by noting how scientific journals present hypothesis tests, including reporting actual p-values rather than just decisions.

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Hss4303b mortality and morbidity

This document discusses a systematic review and meta-analysis on the relationship between dietary fat intake and breast cancer risk. The meta-analysis included 45 studies with over 25,000 breast cancer patients. It found a small increased risk of breast cancer associated with higher total fat intake. The review also discusses terms related to systematic reviews and meta-analysis such as heterogeneity statistics, I2, and the Q statistic.

1.1 statistical and critical thinking

This document provides an overview and objectives of Chapter 1: Introduction to Statistics from an elementary statistics textbook. It covers key statistical concepts like data, population, sample, variables, and the two branches of statistics - descriptive and inferential. Potential pitfalls in statistical analysis like misleading conclusions, biased samples, and nonresponse are also discussed. Examples are provided to illustrate concepts like voluntary response samples, statistical versus practical significance, and interpreting correlation.

Week 15 PowerPoint

This document discusses constructing confidence intervals for estimating population proportions from sample data. It provides three examples of confidence intervals reported in media sources and explains how a 95% confidence interval for a proportion is calculated. The interval is formed by taking the sample proportion plus or minus two standard deviations, where the standard deviation formula substitutes the sample proportion for the unknown true population proportion. The confidence interval estimates a range of values that has a 95% chance of containing the actual population proportion.

Pp chapter 02 reading the news revised

This document contains four hypothetical news articles that lack critical details needed to properly evaluate the research being reported on. It also discusses seven components that readers should consider to thoughtfully assess news reports of data and studies. These include considering the source of funding, the researchers involved, the participants and measurements, and looking for possible biases or limitations. Understanding these components can help readers distinguish well-conducted research from misleading or oversimplified claims.

PUH 5302, Applied Biostatistics 1 Course Learning Outcomes.docx

PUH 5302, Applied Biostatistics 1
Course Learning Outcomes for Unit III
Upon completion of this unit, students should be able to:
4. Recommend solutions to public health problems using biostatistical methods.
4.1 Compute and interpret probability for biostatistical analysis.
4.2 Draw conclusions about public health problems based on biostatistical methods.
5. Analyze public health information to interpret results of biostatistical analysis.
5.1 Analyze literature related to biostatistical analysis in the public health field.
5.2 Prepare an annotated bibliography that explores a topic related to public health issues.
Course/Unit
Learning Outcomes
Learning Activity
4.1
Unit Lesson
Chapter 5
Unit III Problem Solving
4.2
Unit Lesson
Chapter 5
Unit III Problem Solving
5.1
Chapter 5
Unit III Annotated Bibliography
5.2
Chapter 5
Unit III Annotated Bibliography
Reading Assignment
Chapter 5: The Role of Probability
Unit Lesson
Welcome to Unit III. In previous units, we discussed some fundamentals of biostatistics and their application
to solving public health problems. In Unit III, we will compute, interpret, and apply probability, especially in
relation to different populations.
Computing and Interpreting Probabilities
Probability means using a number (or numbers) to demonstrate how likely something is to occur. For
example, if a coin is tossed, the probability of getting a heads or tail is one out of two chances; that is ½.
Researchers have used probability studies to predict weather and other events and have been successful to
some extent. Public health professionals have used statistical methods to predict the chances of health-
related events, thereby providing arguments in favor of taking precautionary measures and warning the
general public on important health issues.
In biostatistics, we use both descriptive statistics and inferential statistics to address public health issues
within a population. In most cases, researchers are not able to study the entire population; they try to get a
sample from the population from which they can generalize their findings.
Descriptive Statistics
Aside from the use of probability sampling methods, there are other methods used for the computation and
interpretation of data; these are generally known as descriptive statistics. With descriptive statistics, we
UNIT III STUDY GUIDE
Probability
PUH 5302, Applied Biostatistics 2
UNIT x STUDY GUIDE
Title
normally compute the mean, mode, median, variance, and standard deviation. Information obtained using
such computation methods is used for descriptive purposes, as opposed to information obtained from
inferential statistics.
Let’s examine this example using the numbers 5, 10, 2, 4, 6, 10, 2, 3, and 2.
The mean is the sum of all the numbers ÷ the number of cases
= 37 ÷ 9
= 4.11
The median is the middle number after the numbers have been arranged in an ascending or descend ...

UNDERSTANDING AND EVALUATINGR E S E A R C HPart II.docx

UNDERSTANDING
AND EVALUATING
R E S E A R C H
Part II
\
Tips for Critical Thinking and Review
BY M A T T BRZYCKI
4 6 A M E R I C A N FITNESS NOVEMBER/DECEMBER 2 0 1 4
P
a rt one o f this article in the
M ayl]une 2014 issue, discussed
the best guidelines f o r evaluat
ing research a n d a n sw e re d
im portant questions to consider
before accepting research as
legitim ate. W e c o n tin u e now
w ith question six.
6. Was the methodology
(experimental protocol) unbiased?
A graphic example of biased methodology
was illustrated in research utilizing 34 Divi
sion III football players.13 In the study, the
athletes were randomly assigned to one of
two experimental groups: a single-set group
or a multiple-set strength/power training
group. In addition to being tasked with us
ing different numbers of sets, the two groups
were also assigned to utilize diverse repeti
tion ranges, equipment, exercises, volume of
exercises and different rest intervals between
sets. A large number of independent variables
makes it impossible to compare the results of
two groups and draw valid conclusions. In
deed, there’s no way to tell which variable was
responsible for the effect.
Notably in this particular study, pre- and
post-testing included the hang clean, an ex
ercise that was featured in the program of
the multiple-set group but not the single-set
group. In effect, the multiple-set group prac
ticed this exercise twice per week for 14 weeks
(as well as a highly related movement—a m id
thigh pull—once per week) while the single
set group had no practice whatsoever. This
gave the multiple-set group much greater fa
miliarity with the hang clean and, as a result,
placed the single-set group at a severe disad
vantage when it came to being post-tested in
that exercise.
7. Did the study account for low respond
ers, high responders and other outliers?
An outlier is numerically distant from the
rest of the data. The fact of the matter is that
some individuals will show little or no im
provement from a product or a program while
others will show exceptional improvement.
Consider the HERITAGE (HEalth, Risk
factors, exercise Training And GEnetics) Fam
ily Study, in which 481 individuals from 98
two-generation families did aerobic training
on stationary bicycles three times per week for
20 weeks.6 On average, the subjects increased
their oxygen intake by about 0.4 liters per m in
ute (L/min). However, their response to train
ing ranged from literally no change to more
than 1.0 L/min, which was 2.5 times the aver
age improvement.
Also worth noting is the FAMuSS (Func
tional Polymorphisms Associated With Hu
man Muscle Size and Strength) Study, wherein
585 individuals did strength training with
their nondominant arm for 12 weeks.10 On
average, the subjects increased their cross-
sectional area by 3.2 centimeters squared (cm2),
their isometric strength by 7.5 kilograms (kg)
and their dynamic strength b ...

40007 chapter8

Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to determine whether to reject the null hypothesis.

Hypothesis testing - Primer

Researchers use hypothesis testing to evaluate claims about populations by taking samples and analyzing the results. The four steps of hypothesis testing are: 1) stating the null and alternative hypotheses, 2) setting the significance level typically at 5%, 3) computing a test statistic to quantify how unlikely the sample results would be if the null was true, and 4) making a decision to either reject or fail to reject the null hypothesis based on comparing the test statistic to the significance level. The goal is to systematically evaluate whether a hypothesized population parameter, such as a mean, is likely to be true based on the sample results.

Hss4303b mortality and morbidity

This document discusses a systematic review and meta-analysis on the relationship between dietary fat intake and breast cancer risk. The meta-analysis included 45 studies with over 25,000 breast cancer patients. It found a small increased risk of breast cancer associated with higher total fat intake. The review also discusses terms related to systematic reviews and meta-analysis such as heterogeneity statistics, I2, and the Q statistic.

1.1 statistical and critical thinking

This document provides an overview and objectives of Chapter 1: Introduction to Statistics from an elementary statistics textbook. It covers key statistical concepts like data, population, sample, variables, and the two branches of statistics - descriptive and inferential. Potential pitfalls in statistical analysis like misleading conclusions, biased samples, and nonresponse are also discussed. Examples are provided to illustrate concepts like voluntary response samples, statistical versus practical significance, and interpreting correlation.

Week 15 PowerPoint

This document discusses constructing confidence intervals for estimating population proportions from sample data. It provides three examples of confidence intervals reported in media sources and explains how a 95% confidence interval for a proportion is calculated. The interval is formed by taking the sample proportion plus or minus two standard deviations, where the standard deviation formula substitutes the sample proportion for the unknown true population proportion. The confidence interval estimates a range of values that has a 95% chance of containing the actual population proportion.

Pp chapter 02 reading the news revised

This document contains four hypothetical news articles that lack critical details needed to properly evaluate the research being reported on. It also discusses seven components that readers should consider to thoughtfully assess news reports of data and studies. These include considering the source of funding, the researchers involved, the participants and measurements, and looking for possible biases or limitations. Understanding these components can help readers distinguish well-conducted research from misleading or oversimplified claims.

PUH 5302, Applied Biostatistics 1 Course Learning Outcomes.docx

PUH 5302, Applied Biostatistics 1
Course Learning Outcomes for Unit III
Upon completion of this unit, students should be able to:
4. Recommend solutions to public health problems using biostatistical methods.
4.1 Compute and interpret probability for biostatistical analysis.
4.2 Draw conclusions about public health problems based on biostatistical methods.
5. Analyze public health information to interpret results of biostatistical analysis.
5.1 Analyze literature related to biostatistical analysis in the public health field.
5.2 Prepare an annotated bibliography that explores a topic related to public health issues.
Course/Unit
Learning Outcomes
Learning Activity
4.1
Unit Lesson
Chapter 5
Unit III Problem Solving
4.2
Unit Lesson
Chapter 5
Unit III Problem Solving
5.1
Chapter 5
Unit III Annotated Bibliography
5.2
Chapter 5
Unit III Annotated Bibliography
Reading Assignment
Chapter 5: The Role of Probability
Unit Lesson
Welcome to Unit III. In previous units, we discussed some fundamentals of biostatistics and their application
to solving public health problems. In Unit III, we will compute, interpret, and apply probability, especially in
relation to different populations.
Computing and Interpreting Probabilities
Probability means using a number (or numbers) to demonstrate how likely something is to occur. For
example, if a coin is tossed, the probability of getting a heads or tail is one out of two chances; that is ½.
Researchers have used probability studies to predict weather and other events and have been successful to
some extent. Public health professionals have used statistical methods to predict the chances of health-
related events, thereby providing arguments in favor of taking precautionary measures and warning the
general public on important health issues.
In biostatistics, we use both descriptive statistics and inferential statistics to address public health issues
within a population. In most cases, researchers are not able to study the entire population; they try to get a
sample from the population from which they can generalize their findings.
Descriptive Statistics
Aside from the use of probability sampling methods, there are other methods used for the computation and
interpretation of data; these are generally known as descriptive statistics. With descriptive statistics, we
UNIT III STUDY GUIDE
Probability
PUH 5302, Applied Biostatistics 2
UNIT x STUDY GUIDE
Title
normally compute the mean, mode, median, variance, and standard deviation. Information obtained using
such computation methods is used for descriptive purposes, as opposed to information obtained from
inferential statistics.
Let’s examine this example using the numbers 5, 10, 2, 4, 6, 10, 2, 3, and 2.
The mean is the sum of all the numbers ÷ the number of cases
= 37 ÷ 9
= 4.11
The median is the middle number after the numbers have been arranged in an ascending or descend ...

UNDERSTANDING AND EVALUATINGR E S E A R C HPart II.docx

UNDERSTANDING
AND EVALUATING
R E S E A R C H
Part II
\
Tips for Critical Thinking and Review
BY M A T T BRZYCKI
4 6 A M E R I C A N FITNESS NOVEMBER/DECEMBER 2 0 1 4
P
a rt one o f this article in the
M ayl]une 2014 issue, discussed
the best guidelines f o r evaluat
ing research a n d a n sw e re d
im portant questions to consider
before accepting research as
legitim ate. W e c o n tin u e now
w ith question six.
6. Was the methodology
(experimental protocol) unbiased?
A graphic example of biased methodology
was illustrated in research utilizing 34 Divi
sion III football players.13 In the study, the
athletes were randomly assigned to one of
two experimental groups: a single-set group
or a multiple-set strength/power training
group. In addition to being tasked with us
ing different numbers of sets, the two groups
were also assigned to utilize diverse repeti
tion ranges, equipment, exercises, volume of
exercises and different rest intervals between
sets. A large number of independent variables
makes it impossible to compare the results of
two groups and draw valid conclusions. In
deed, there’s no way to tell which variable was
responsible for the effect.
Notably in this particular study, pre- and
post-testing included the hang clean, an ex
ercise that was featured in the program of
the multiple-set group but not the single-set
group. In effect, the multiple-set group prac
ticed this exercise twice per week for 14 weeks
(as well as a highly related movement—a m id
thigh pull—once per week) while the single
set group had no practice whatsoever. This
gave the multiple-set group much greater fa
miliarity with the hang clean and, as a result,
placed the single-set group at a severe disad
vantage when it came to being post-tested in
that exercise.
7. Did the study account for low respond
ers, high responders and other outliers?
An outlier is numerically distant from the
rest of the data. The fact of the matter is that
some individuals will show little or no im
provement from a product or a program while
others will show exceptional improvement.
Consider the HERITAGE (HEalth, Risk
factors, exercise Training And GEnetics) Fam
ily Study, in which 481 individuals from 98
two-generation families did aerobic training
on stationary bicycles three times per week for
20 weeks.6 On average, the subjects increased
their oxygen intake by about 0.4 liters per m in
ute (L/min). However, their response to train
ing ranged from literally no change to more
than 1.0 L/min, which was 2.5 times the aver
age improvement.
Also worth noting is the FAMuSS (Func
tional Polymorphisms Associated With Hu
man Muscle Size and Strength) Study, wherein
585 individuals did strength training with
their nondominant arm for 12 weeks.10 On
average, the subjects increased their cross-
sectional area by 3.2 centimeters squared (cm2),
their isometric strength by 7.5 kilograms (kg)
and their dynamic strength b ...

40007 chapter8

Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to determine whether to reject the null hypothesis.

Hypothesis testing - Primer

Researchers use hypothesis testing to evaluate claims about populations by taking samples and analyzing the results. The four steps of hypothesis testing are: 1) stating the null and alternative hypotheses, 2) setting the significance level typically at 5%, 3) computing a test statistic to quantify how unlikely the sample results would be if the null was true, and 4) making a decision to either reject or fail to reject the null hypothesis based on comparing the test statistic to the significance level. The goal is to systematically evaluate whether a hypothesized population parameter, such as a mean, is likely to be true based on the sample results.

Chapter8 introduction to hypothesis testing

Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to determine whether to reject the null hypothesis.

Hypothesis test

Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject or fail to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to either reject or fail to reject the null hypothesis.

Aron chpt 5 ed revised

This document provides an overview of hypothesis testing, including:
1) It defines hypothesis testing theory and the hypothesis testing process involving null and research hypotheses.
2) It explains the core logic of hypothesis testing including determining characteristics of the comparison distribution, setting cutoff sample scores, and deciding whether to reject the null hypothesis.
3) It discusses one-tailed and two-tailed hypothesis tests and the types of decision errors that can occur in hypothesis testing.

Aron chpt 5 ed

This document provides an overview of hypothesis testing, including:
1) The core logic involves forming a research hypothesis and null hypothesis, determining cutoff criteria, analyzing sample results, and deciding whether to reject the null hypothesis.
2) The hypothesis testing process involves restating the research question, determining sampling distributions, setting cutoff scores, analyzing sample scores, and making a decision.
3) Type I and Type II errors can occur when making decisions, where the wrong hypothesis is accepted or rejected. Setting significance levels balances these errors.

The Research Process

This document summarizes a statistics lecture about the research process and why statistics are needed in optometry and vision science. It discusses the steps of evidence-based practice including asking questions, acquiring evidence, appraising evidence, and applying evidence. It also covers generating and testing theories, levels of measurement, measurement error, validity, reliability, types of research such as correlational and experimental research, and methods of data collection and analysis. The goal is to explain the research process and why statistics are an essential tool for evidence-based practice in optometry.

Sample size calculation

1. Sample size calculation is an important part of ethical scientific research to avoid underpowered studies.
2. There are different approaches to sample size calculation depending on the study design and endpoints, such as comparing proportions, estimating confidence intervals, or analyzing time to event outcomes.
3. Key steps include defining the research hypothesis, primary and secondary endpoints, how and in whom the endpoints will be measured, and determining what difference is clinically meaningful to detect between study groups.

Brmedj00047 0038

1) The article discusses the importance of properly determining sample size in medical research studies. Sample size is one of the most common reasons researchers consult statisticians.
2) Studies with too small of a sample size will likely be unable to detect clinically important effects and thus be scientifically useless and unethical. However, studies with unnecessarily large samples can also be deemed unethical due to unnecessary involvement of extra subjects.
3) The concept of statistical power, which is the likelihood of a test detecting a true difference or effect of a given size, is important for determining an appropriate sample size that balances scientific validity with ethical considerations. Methods like power calculations, graphs, and nomograms can help researchers prospectively determine adequate

1. In a study, 28 adults with mild periodontal disease are assesse

1. In a study, 28 adults with mild periodontal disease are assessed before and 6 months after the implementation of a dental-education program intended to promote better oral hygiene. After 6 months, periodontal status improved in 15 patients, declined in 8, and remained the same in 5.
Choose one answer:
Assess the impact of the program statistically (use a two-sided test).
We do not reject H_0 at the 5% level and conclude that patients have not significantly changed on the program.
We reject H_0 at the 5% level and conclude that patients have significantly changed on the program.
We reject H_0 at the 5% level and conclude that patients have not significantly changed on the program.
We do not reject H_0 at the 5% level and conclude that patients have significantly changed on the program.
2. data can be ordered but do not have specific numeric values. Thus, common arithmetic cannot be performed on ordinal data in a meaningful way.
Choose one answer:
ordinal
interval
ratio
nominal
3. A ______________ design is a type of randomized clinical trial in which each participant is randomized to either group A or group B which receive different treatments, then later switch treatments.
Choose one answer:
cross-over
case-control
prospective
retrospective
4. Suppose researchers do an epidemiologic investigation of people entering a sexually transmitted disease clinic. They find that 160 of 200 patients who are diagnosed as having gonorrhea and 50 of 105 patients who are diagnosed as having nongonococcal urethritis have had previous episodes of urethritis.
Are the present diagnosis and prior episodes of urethritis associated? (Hint: a chi-square test with Yates' correction)
Choose one answer:
Gonorrhea patients are significantly more likely to have prior episodes of urethritis than NGU patients.
Gonorrhea patients are not significantly more likely to have prior episodes of urethritis than NGU patients.
5. The _______ rate is defined as the proportion of participants in the placebo group who actually receive the active treatment outside the study protocol.
6. I DID IT
7. I DID IT
8. I DID IT
9. For any sample point ( x subscript i, y subscript i), the ___________________ of that point about the regression line is defined by ( stack y subscript i with hat on top minus top enclose y). (Hint: the blank is two words)
10. The following statistics are taken from an article by Burch relating cigarette smoking to lung cancer. The article presents data relating mortality from lung cancer to average cigarette consumption (lb/person) for females in England and Wales over a 40-year period. The data are given in the table below.
Cigarette consumption and lung-cancer mortality in England and Wales, 1930-1969
Period
log_{10} mortality (over 5 years), y
log_{10} annual cigarette consumption (lb/person), x
1930-1934
-2.35
-0.26
1935-1939
-2.20
-0.03
1940-1944
-2.12
0.30
1945-1949
-1.95
0.37
1950-1954
-1.85
0.40
1955-1959
-1.80
0.50
1960-1 ...

Research methodology 101

1. The document discusses common pitfalls in research studies related to reproductive medicine and how to avoid them.
2. Key pitfalls include problems with study design, sampling, operationalization, and generalizability. Randomized controlled trials (RCTs) are recommended to properly assess treatment efficacy.
3. When conducting RCTs, intention-to-treat analysis and accounting for loss to follow up are important to avoid bias. The primary outcome measure and unit of analysis must also be appropriately defined.

Statistical and critical thinking

This document discusses key concepts in statistical and critical thinking. It defines important statistical terms like population, sample, data, parameters and statistics. It explains how to analyze sampling data by considering the context, source and sampling methods. It also distinguishes between statistical significance, which indicates unlikely results, and practical significance, which considers if findings make an meaningful difference. The document provides examples of how to identify populations and samples, and outlines the steps of preparing, analyzing and concluding when doing statistics. It discusses issues with voluntary response samples and concludes with an example comparing statistical and practical significance.

UkNSCC 2016 CochraneTAG workshop

Workshop delivered by the Cochrane Tobacco Addiction Group's Managing Editors at the UKNSCC conference 2016 in London, UK

Cohort studies

Overveiw of Cohort studies ; steps involved in conducting Cohort studies, Advantages & disadvanges ; Notable examples of different types of cohorts.

Ch 2 basic concepts in pharmacoepidemiology (10 hrs)

This document outlines key concepts in pharmacoepidemiology study designs. It discusses the scientific method and different types of epidemiologic study designs including descriptive studies, analytical studies, case reports, case series, ecological studies, cross-sectional studies, case-control studies, and cohort studies. It emphasizes that randomized controlled trials provide the strongest evidence while case reports and ecological studies provide the weakest evidence.

Chapter 3 part1-Design of Experiments

This document provides an introduction to experimental design and sampling methods used to produce data for statistical analysis. It discusses the differences between observational studies and experimental studies, as well as key concepts in experimental design including randomization, control groups, placebos, and blocking/stratification. Specific experimental designs covered include completely randomized designs, blocked/stratified designs, and matched pairs designs. Examples are provided to illustrate how different experimental designs can be applied.

There’s No Escape from External Validity – Reporting Habits of Randomized Con...

There’s No Escape from External Validity – Reporting Habits of Randomized Con...Stockholm Institute of Transition Economics

Version March 13, 2015 – Please contact authors for an updated version before citing
Randomized Controlled Trials (RCT) are considered the gold standard in empirical social sciences and have been increasingly used in recent years. While their internal validity is in most cases beyond discussion, RCTs still need to establish external validity. External validity is the crucial determinant for a study’s policy relevance and might be at stake because of potential general equilibrium effects, Hawthorne effects,
or representativeness problems that compromise generalizing results beyond the studied population. For this paper, we reviewed all RCTs published in leading economic journals between 2009 and 2012 and scrutinized them for the way in which they treat external validity. Based on a set of objective indicators, we find that the RCT literature does not adequately account for potential hazards to external validity. A large part of published RCTs does not discuss potential limitations to external validity or provide the information that is necessary to assess potential problems. We conclude by calling for a more systematic approach to designing RCTs and to reporting the results.NORMAN, ELTON_BTM7303-12-82NORMAN, ELTON_BTM7303-12-81.docx

NORMAN, ELTON_BTM7303-12-8 2
NORMAN, ELTON_BTM7303-12-8 1
Hello Elton,
I appreciate your note. YES. Keep trying. I know that making the transition to doctoral-level reasoning can be hard! It was very hard for me in some areas because it seemed … unnatural. Does that make sense? Some aspects of this type of thinking seemed “clunky” and hard to explain in plain language. I wanted research problems, research purpose statements, etc. to simply flow. In the beginning of my journey there was very little flow (more like trickles) and lots of missteps!
For this assignment, you were asked to build on your assignment last week to further explore how you might examine your research problem using a quantitative methodology. You were required to respond to these questions:
· Please restate the research problem, purpose, and research questions you developed previously and incorporate any faculty feedback as appropriate. This week be sure to also include hypotheses for each of your research questions.
· How might surveys be used to answer your research questions? What are the advantages and disadvantages of using surveys to collect data?
· How might you use an experiment or quasi-experiment to answer your research questions? What are the advantages and disadvantages of using (quasi)experiments to collect your data?
· It is also important to consider how you might analyze the potential data you collect and factors that could affect those analyses. Specifically, what are Type I and Type II errors? How might these impact your study? What is statistical power? How might this impact your study? What steps can you take ahead of time to help avoid issues related to Type I & II errors as well as power?
As part of our standard, you were also required to use scholarly sources to support all assertions and research decisions.
Length: 5 to 7 pages, not including title and reference pages
I used the rubric below to assess your submission. As I moved through each section of your paper, I looked for information that demonstrated you understood important research terms such as hypothesis, null hypothesis, Type I and Type II Errors and statistical power. In most instances you demonstrated some understanding of these concepts or terms. In several instances your understanding hindered your ability to create rigorous hypotheses because there were aspects of these terms that remained unclear. I added several prompts and questions to help you in these areas.
Grading Rubric
Criteria
Content (4 points)
Points
1
State research problem, purpose, research questions and hypotheses
1.5/2
2
Discussed in detail the advantages and disadvantages of using surveys to collect data
.75/ 1
3
Explained how you could use experiments or quasi-experiments to collect data for your study and the advantages and disadvantages of these designs
.75/1
Organization (1 point)
4
Organized and presented in a clear manner. Included a minimum of five scholarly references, with appropri.

QUESTION 11. Place the following 5 overarching steps in a resear.docx

QUESTION 1
1. Place the following 5 overarching steps in a research project in the order they should happen.
Report Findings
Design Study and Collect Data
Identify Study Question
Analyze Data
Select Study Approach
0.35 points
QUESTION 2
1. A study to answer the question, "What is the diabetes prevalence among Native Americans in Maine?" is mostly likely employing which of the following designs?
Cross-sectional
Case-control
Randomized Clinical Trial
Pretest-Post Test Nonequivalent Group Design
0.35 points
QUESTION 3
1. The specific aim statement, "To assess whether more exercise is better for overweight children" is...?
Too Precise
Too Vague
Just Right
0.3 points
QUESTION 4
1. Studies that randomize participants into intervention or control groups can still meet the "distributive justice" principle even though not all the participants receive the intervention.
True
False
0.3 points
QUESTION 5
1. When we figure out how to turn a construct that we want to include in an index into some sort of quantitative score, we are FILL IN THE BLANK the construct.
Validating
Abstracting
Operationalizing
Weighting
0.3 points
QUESTION 6
1. The quasi-experimental design that most closely matches a pretest-post test randomized experiment, only without randomizing the groups is a...
Case-control
Randomized clinical trial
Cross-sectional
Pretest-post test nonequivalent group design
0.35 points
QUESTION 7
1. Researchers are conducting an analysis using a de-identified dataset that was created at a hospital for quality reporting purposes. There is a good chance that the IRB would consider this study "exempt" on the grounds that:
It would likely not meet the "beneficence" principle.
The informed consent process would not be feasible.
The research involves analysis of an existing dataset.
All research that does not involve blatant human rights abuses is considered.
0.35 points
QUESTION 8
1. What is the most confusing or unclear thing we have discussed thus far? Type II Error and Power 1 paragraph
Path: p
Words:0
0.4 points
QUESTION 9
1. A study on a new depression treatment recruited patients who scored in the highest depression range ("severely depressed") on the PHQ9 depression screen questionnaire. These patients were assigned to either a treatment or a comparison group. At the end of the study the participants retook the PHQ9, and it was observed that the scores improved in both study groups. This is probably an example of:
A Reliability Threat
Regression Threat
Instrumentation Threat
A Threat to External Validity
0.35 points
QUESTION 10
1. If the primary outcome of your study perfectly captures the concept you are studying, you could say that your study has excellent...
External Validity
Reliability
Construct Validity
None of the A ...

EBM_2016.pdf

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N#05

This document discusses developing research questions and hypotheses. It defines key terms like variables, research questions, null and research hypotheses. Research questions can be structured or unstructured and focus on treatment, prognosis, causation or etiology. Hypotheses are tentative statements about relationships between variables and come in different types like simple/complex, associative/causal, directional/non-directional, and null/research. The type of research design impacts how hypotheses are worded, with experimental designs implying cause-effect relationships and non-experimental associative relationships. Hypotheses can be supported or rejected based on study findings.

Introduction to Clinical Epidemiology (401173) FINAL ASSIGNMENT

Introduction to Clinical Epidemiology (401173)
FINAL ASSIGNMENT
Autumn, 2019
Due date: 11.59pm , May 29 2019
This assignment is based on the learning objectives and concepts as described in the Unit Learning Guide. There are 9 questions worth a total of 64 marks and this assignment will contribute 64% towards the total assessment for this subject.
Your assignment should be typed, with adequate space left between questions. Assignments should be submitted via vUWS. Be as concise as possible in your answers, and use the number of marks allocated to each question as a guide for how much to write.
Please note this is an individual exercise.
Late assignments will not be accepted without prior approval.
You are required to answer ALL questions (1-9)
Page 1 of 7
Answer questions 1-2 based on the following scenarios:
Q1: Fred, a 65-year-old obese man with a history of type 2 diabetes mellitus and hypertension presents to the GP practice for a follow-up appointment. During the consultation, he asks whether there is a better medication to glicazide and metformin, his oral hypoglycemic medications, which he has been taking to control his blood sugar. His friend has recently been put on a newer oral hypoglycemic medication (Liraglutide, a glucagon-like peptide-1 analogue), which has been shown to help with weight management in patients with diabetes and obesity. Fred has been finding it very difficult to lose weight for a few years now as he has tried various lifestyle modifications. He asks whether the new oral hypoglycemic medications could be an option for him in weight reduction.
Task [2 marks]
a. Write a focused research question for this particular problem that will help you organise a search of the literature for an answer (use the PICO elements as appropriate).
b. Identify the PICO elements in your research question
Q2: In the past 2 years, as an Infectious Disease Specialist in one of the tertiary hospitals in Australia, you have attended to 23 migrant patients who were referred by their General Practitioners with symptoms not typical of pulmonary tuberculosis. After taking a detailed history and performing appropriate physical examinations, as well as reviewing a range of relevant investigations, you clinically diagnosed and microbiologically confirmed that those patients have multi-drug resistance pulmonary tuberculosis (MDR-TB). The Public Health Department was notified of disease and the patients were managed accordingly. Now, you and some colleagues from Western Sydney University want to investigate the risk factors for MDR-TB.
Task [2 marks]
a. Write a focused research question for this particular problem that will help you organise a search of the literature for an answer (use the PICO elements as appropriate).
b. Identify the PICO elements in your research question
Q3: Please select the single best answer for each of questions 3.I – VII
I. Randomised controlled trials ...

sesion1.ppt

Este documento presenta una introducción a la econometría. Explica que la econometría ayuda a combinar la teoría económica y los datos económicos para tomar mejores decisiones económicas. También describe los modelos econométricos básicos, incluido el modelo de regresión lineal simple, y los supuestos subyacentes a estos modelos. Finalmente, introduce conceptos clave como el término de error y los mínimos cuadrados ordinarios.

Chapter8 introduction to hypothesis testingResearchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to determine whether to reject the null hypothesis.

Hypothesis test

Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject or fail to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to either reject or fail to reject the null hypothesis.

Aron chpt 5 ed revised

This document provides an overview of hypothesis testing, including:
1) It defines hypothesis testing theory and the hypothesis testing process involving null and research hypotheses.
2) It explains the core logic of hypothesis testing including determining characteristics of the comparison distribution, setting cutoff sample scores, and deciding whether to reject the null hypothesis.
3) It discusses one-tailed and two-tailed hypothesis tests and the types of decision errors that can occur in hypothesis testing.

Aron chpt 5 ed

This document provides an overview of hypothesis testing, including:
1) The core logic involves forming a research hypothesis and null hypothesis, determining cutoff criteria, analyzing sample results, and deciding whether to reject the null hypothesis.
2) The hypothesis testing process involves restating the research question, determining sampling distributions, setting cutoff scores, analyzing sample scores, and making a decision.
3) Type I and Type II errors can occur when making decisions, where the wrong hypothesis is accepted or rejected. Setting significance levels balances these errors.

The Research Process

This document summarizes a statistics lecture about the research process and why statistics are needed in optometry and vision science. It discusses the steps of evidence-based practice including asking questions, acquiring evidence, appraising evidence, and applying evidence. It also covers generating and testing theories, levels of measurement, measurement error, validity, reliability, types of research such as correlational and experimental research, and methods of data collection and analysis. The goal is to explain the research process and why statistics are an essential tool for evidence-based practice in optometry.

Sample size calculation

1. Sample size calculation is an important part of ethical scientific research to avoid underpowered studies.
2. There are different approaches to sample size calculation depending on the study design and endpoints, such as comparing proportions, estimating confidence intervals, or analyzing time to event outcomes.
3. Key steps include defining the research hypothesis, primary and secondary endpoints, how and in whom the endpoints will be measured, and determining what difference is clinically meaningful to detect between study groups.

Brmedj00047 0038

1) The article discusses the importance of properly determining sample size in medical research studies. Sample size is one of the most common reasons researchers consult statisticians.
2) Studies with too small of a sample size will likely be unable to detect clinically important effects and thus be scientifically useless and unethical. However, studies with unnecessarily large samples can also be deemed unethical due to unnecessary involvement of extra subjects.
3) The concept of statistical power, which is the likelihood of a test detecting a true difference or effect of a given size, is important for determining an appropriate sample size that balances scientific validity with ethical considerations. Methods like power calculations, graphs, and nomograms can help researchers prospectively determine adequate

1. In a study, 28 adults with mild periodontal disease are assesse

1. In a study, 28 adults with mild periodontal disease are assessed before and 6 months after the implementation of a dental-education program intended to promote better oral hygiene. After 6 months, periodontal status improved in 15 patients, declined in 8, and remained the same in 5.
Choose one answer:
Assess the impact of the program statistically (use a two-sided test).
We do not reject H_0 at the 5% level and conclude that patients have not significantly changed on the program.
We reject H_0 at the 5% level and conclude that patients have significantly changed on the program.
We reject H_0 at the 5% level and conclude that patients have not significantly changed on the program.
We do not reject H_0 at the 5% level and conclude that patients have significantly changed on the program.
2. data can be ordered but do not have specific numeric values. Thus, common arithmetic cannot be performed on ordinal data in a meaningful way.
Choose one answer:
ordinal
interval
ratio
nominal
3. A ______________ design is a type of randomized clinical trial in which each participant is randomized to either group A or group B which receive different treatments, then later switch treatments.
Choose one answer:
cross-over
case-control
prospective
retrospective
4. Suppose researchers do an epidemiologic investigation of people entering a sexually transmitted disease clinic. They find that 160 of 200 patients who are diagnosed as having gonorrhea and 50 of 105 patients who are diagnosed as having nongonococcal urethritis have had previous episodes of urethritis.
Are the present diagnosis and prior episodes of urethritis associated? (Hint: a chi-square test with Yates' correction)
Choose one answer:
Gonorrhea patients are significantly more likely to have prior episodes of urethritis than NGU patients.
Gonorrhea patients are not significantly more likely to have prior episodes of urethritis than NGU patients.
5. The _______ rate is defined as the proportion of participants in the placebo group who actually receive the active treatment outside the study protocol.
6. I DID IT
7. I DID IT
8. I DID IT
9. For any sample point ( x subscript i, y subscript i), the ___________________ of that point about the regression line is defined by ( stack y subscript i with hat on top minus top enclose y). (Hint: the blank is two words)
10. The following statistics are taken from an article by Burch relating cigarette smoking to lung cancer. The article presents data relating mortality from lung cancer to average cigarette consumption (lb/person) for females in England and Wales over a 40-year period. The data are given in the table below.
Cigarette consumption and lung-cancer mortality in England and Wales, 1930-1969
Period
log_{10} mortality (over 5 years), y
log_{10} annual cigarette consumption (lb/person), x
1930-1934
-2.35
-0.26
1935-1939
-2.20
-0.03
1940-1944
-2.12
0.30
1945-1949
-1.95
0.37
1950-1954
-1.85
0.40
1955-1959
-1.80
0.50
1960-1 ...

Research methodology 101

1. The document discusses common pitfalls in research studies related to reproductive medicine and how to avoid them.
2. Key pitfalls include problems with study design, sampling, operationalization, and generalizability. Randomized controlled trials (RCTs) are recommended to properly assess treatment efficacy.
3. When conducting RCTs, intention-to-treat analysis and accounting for loss to follow up are important to avoid bias. The primary outcome measure and unit of analysis must also be appropriately defined.

Statistical and critical thinking

This document discusses key concepts in statistical and critical thinking. It defines important statistical terms like population, sample, data, parameters and statistics. It explains how to analyze sampling data by considering the context, source and sampling methods. It also distinguishes between statistical significance, which indicates unlikely results, and practical significance, which considers if findings make an meaningful difference. The document provides examples of how to identify populations and samples, and outlines the steps of preparing, analyzing and concluding when doing statistics. It discusses issues with voluntary response samples and concludes with an example comparing statistical and practical significance.

UkNSCC 2016 CochraneTAG workshop

Workshop delivered by the Cochrane Tobacco Addiction Group's Managing Editors at the UKNSCC conference 2016 in London, UK

Cohort studies

Overveiw of Cohort studies ; steps involved in conducting Cohort studies, Advantages & disadvanges ; Notable examples of different types of cohorts.

Ch 2 basic concepts in pharmacoepidemiology (10 hrs)

This document outlines key concepts in pharmacoepidemiology study designs. It discusses the scientific method and different types of epidemiologic study designs including descriptive studies, analytical studies, case reports, case series, ecological studies, cross-sectional studies, case-control studies, and cohort studies. It emphasizes that randomized controlled trials provide the strongest evidence while case reports and ecological studies provide the weakest evidence.

Chapter 3 part1-Design of Experiments

This document provides an introduction to experimental design and sampling methods used to produce data for statistical analysis. It discusses the differences between observational studies and experimental studies, as well as key concepts in experimental design including randomization, control groups, placebos, and blocking/stratification. Specific experimental designs covered include completely randomized designs, blocked/stratified designs, and matched pairs designs. Examples are provided to illustrate how different experimental designs can be applied.

There’s No Escape from External Validity – Reporting Habits of Randomized Con...

There’s No Escape from External Validity – Reporting Habits of Randomized Con...Stockholm Institute of Transition Economics

Version March 13, 2015 – Please contact authors for an updated version before citing
Randomized Controlled Trials (RCT) are considered the gold standard in empirical social sciences and have been increasingly used in recent years. While their internal validity is in most cases beyond discussion, RCTs still need to establish external validity. External validity is the crucial determinant for a study’s policy relevance and might be at stake because of potential general equilibrium effects, Hawthorne effects,
or representativeness problems that compromise generalizing results beyond the studied population. For this paper, we reviewed all RCTs published in leading economic journals between 2009 and 2012 and scrutinized them for the way in which they treat external validity. Based on a set of objective indicators, we find that the RCT literature does not adequately account for potential hazards to external validity. A large part of published RCTs does not discuss potential limitations to external validity or provide the information that is necessary to assess potential problems. We conclude by calling for a more systematic approach to designing RCTs and to reporting the results.NORMAN, ELTON_BTM7303-12-82NORMAN, ELTON_BTM7303-12-81.docx

NORMAN, ELTON_BTM7303-12-8 2
NORMAN, ELTON_BTM7303-12-8 1
Hello Elton,
I appreciate your note. YES. Keep trying. I know that making the transition to doctoral-level reasoning can be hard! It was very hard for me in some areas because it seemed … unnatural. Does that make sense? Some aspects of this type of thinking seemed “clunky” and hard to explain in plain language. I wanted research problems, research purpose statements, etc. to simply flow. In the beginning of my journey there was very little flow (more like trickles) and lots of missteps!
For this assignment, you were asked to build on your assignment last week to further explore how you might examine your research problem using a quantitative methodology. You were required to respond to these questions:
· Please restate the research problem, purpose, and research questions you developed previously and incorporate any faculty feedback as appropriate. This week be sure to also include hypotheses for each of your research questions.
· How might surveys be used to answer your research questions? What are the advantages and disadvantages of using surveys to collect data?
· How might you use an experiment or quasi-experiment to answer your research questions? What are the advantages and disadvantages of using (quasi)experiments to collect your data?
· It is also important to consider how you might analyze the potential data you collect and factors that could affect those analyses. Specifically, what are Type I and Type II errors? How might these impact your study? What is statistical power? How might this impact your study? What steps can you take ahead of time to help avoid issues related to Type I & II errors as well as power?
As part of our standard, you were also required to use scholarly sources to support all assertions and research decisions.
Length: 5 to 7 pages, not including title and reference pages
I used the rubric below to assess your submission. As I moved through each section of your paper, I looked for information that demonstrated you understood important research terms such as hypothesis, null hypothesis, Type I and Type II Errors and statistical power. In most instances you demonstrated some understanding of these concepts or terms. In several instances your understanding hindered your ability to create rigorous hypotheses because there were aspects of these terms that remained unclear. I added several prompts and questions to help you in these areas.
Grading Rubric
Criteria
Content (4 points)
Points
1
State research problem, purpose, research questions and hypotheses
1.5/2
2
Discussed in detail the advantages and disadvantages of using surveys to collect data
.75/ 1
3
Explained how you could use experiments or quasi-experiments to collect data for your study and the advantages and disadvantages of these designs
.75/1
Organization (1 point)
4
Organized and presented in a clear manner. Included a minimum of five scholarly references, with appropri.

QUESTION 11. Place the following 5 overarching steps in a resear.docx

QUESTION 1
1. Place the following 5 overarching steps in a research project in the order they should happen.
Report Findings
Design Study and Collect Data
Identify Study Question
Analyze Data
Select Study Approach
0.35 points
QUESTION 2
1. A study to answer the question, "What is the diabetes prevalence among Native Americans in Maine?" is mostly likely employing which of the following designs?
Cross-sectional
Case-control
Randomized Clinical Trial
Pretest-Post Test Nonequivalent Group Design
0.35 points
QUESTION 3
1. The specific aim statement, "To assess whether more exercise is better for overweight children" is...?
Too Precise
Too Vague
Just Right
0.3 points
QUESTION 4
1. Studies that randomize participants into intervention or control groups can still meet the "distributive justice" principle even though not all the participants receive the intervention.
True
False
0.3 points
QUESTION 5
1. When we figure out how to turn a construct that we want to include in an index into some sort of quantitative score, we are FILL IN THE BLANK the construct.
Validating
Abstracting
Operationalizing
Weighting
0.3 points
QUESTION 6
1. The quasi-experimental design that most closely matches a pretest-post test randomized experiment, only without randomizing the groups is a...
Case-control
Randomized clinical trial
Cross-sectional
Pretest-post test nonequivalent group design
0.35 points
QUESTION 7
1. Researchers are conducting an analysis using a de-identified dataset that was created at a hospital for quality reporting purposes. There is a good chance that the IRB would consider this study "exempt" on the grounds that:
It would likely not meet the "beneficence" principle.
The informed consent process would not be feasible.
The research involves analysis of an existing dataset.
All research that does not involve blatant human rights abuses is considered.
0.35 points
QUESTION 8
1. What is the most confusing or unclear thing we have discussed thus far? Type II Error and Power 1 paragraph
Path: p
Words:0
0.4 points
QUESTION 9
1. A study on a new depression treatment recruited patients who scored in the highest depression range ("severely depressed") on the PHQ9 depression screen questionnaire. These patients were assigned to either a treatment or a comparison group. At the end of the study the participants retook the PHQ9, and it was observed that the scores improved in both study groups. This is probably an example of:
A Reliability Threat
Regression Threat
Instrumentation Threat
A Threat to External Validity
0.35 points
QUESTION 10
1. If the primary outcome of your study perfectly captures the concept you are studying, you could say that your study has excellent...
External Validity
Reliability
Construct Validity
None of the A ...

EBM_2016.pdf

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N#05

This document discusses developing research questions and hypotheses. It defines key terms like variables, research questions, null and research hypotheses. Research questions can be structured or unstructured and focus on treatment, prognosis, causation or etiology. Hypotheses are tentative statements about relationships between variables and come in different types like simple/complex, associative/causal, directional/non-directional, and null/research. The type of research design impacts how hypotheses are worded, with experimental designs implying cause-effect relationships and non-experimental associative relationships. Hypotheses can be supported or rejected based on study findings.

Introduction to Clinical Epidemiology (401173) FINAL ASSIGNMENT

Introduction to Clinical Epidemiology (401173)
FINAL ASSIGNMENT
Autumn, 2019
Due date: 11.59pm , May 29 2019
This assignment is based on the learning objectives and concepts as described in the Unit Learning Guide. There are 9 questions worth a total of 64 marks and this assignment will contribute 64% towards the total assessment for this subject.
Your assignment should be typed, with adequate space left between questions. Assignments should be submitted via vUWS. Be as concise as possible in your answers, and use the number of marks allocated to each question as a guide for how much to write.
Please note this is an individual exercise.
Late assignments will not be accepted without prior approval.
You are required to answer ALL questions (1-9)
Page 1 of 7
Answer questions 1-2 based on the following scenarios:
Q1: Fred, a 65-year-old obese man with a history of type 2 diabetes mellitus and hypertension presents to the GP practice for a follow-up appointment. During the consultation, he asks whether there is a better medication to glicazide and metformin, his oral hypoglycemic medications, which he has been taking to control his blood sugar. His friend has recently been put on a newer oral hypoglycemic medication (Liraglutide, a glucagon-like peptide-1 analogue), which has been shown to help with weight management in patients with diabetes and obesity. Fred has been finding it very difficult to lose weight for a few years now as he has tried various lifestyle modifications. He asks whether the new oral hypoglycemic medications could be an option for him in weight reduction.
Task [2 marks]
a. Write a focused research question for this particular problem that will help you organise a search of the literature for an answer (use the PICO elements as appropriate).
b. Identify the PICO elements in your research question
Q2: In the past 2 years, as an Infectious Disease Specialist in one of the tertiary hospitals in Australia, you have attended to 23 migrant patients who were referred by their General Practitioners with symptoms not typical of pulmonary tuberculosis. After taking a detailed history and performing appropriate physical examinations, as well as reviewing a range of relevant investigations, you clinically diagnosed and microbiologically confirmed that those patients have multi-drug resistance pulmonary tuberculosis (MDR-TB). The Public Health Department was notified of disease and the patients were managed accordingly. Now, you and some colleagues from Western Sydney University want to investigate the risk factors for MDR-TB.
Task [2 marks]
a. Write a focused research question for this particular problem that will help you organise a search of the literature for an answer (use the PICO elements as appropriate).
b. Identify the PICO elements in your research question
Q3: Please select the single best answer for each of questions 3.I – VII
I. Randomised controlled trials ...

Chapter8 introduction to hypothesis testing

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QUESTION 11. Place the following 5 overarching steps in a resear.docx

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EBM_2016.pdf

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N#05

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Introduction to Clinical Epidemiology (401173) FINAL ASSIGNMENT

Introduction to Clinical Epidemiology (401173) FINAL ASSIGNMENT

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1. The document discusses the basic principles of hypothesis testing, including stating the null and alternative hypotheses, selecting a significance level, choosing a test statistic, determining critical values, and making a decision to reject or fail to reject the null hypothesis.
2. It outlines the five steps of hypothesis testing: state hypotheses, select significance level, select test statistic, determine critical value, and make a decision.
3. Key terms discussed include type I and type II errors, significance levels, critical values, test statistics such as z and t, and the decision to reject or fail to reject the null hypothesis.

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This document discusses one-tailed and two-tailed hypothesis tests. It explains that a one-tailed test looks for an increase or decrease in a parameter, while a two-tailed test looks for any change. If the claim uses words like "increased" or "decreased", it is a one-tailed test, while terms like "change" or "different" indicate a two-tailed test. A one-tailed test has a critical region in one tail, while a two-tailed test has critical regions in both tails. The example given is a one-tailed test comparing civil engineering and B.A. salaries, with the alternative hypothesis that engineering salaries are greater than B.A. salaries.

One-tailed-Test-or-Two-tailed-Test.pdf

This document discusses one-tailed and two-tailed hypothesis tests. It explains that a one-tailed test looks for an increase or decrease in a parameter, while a two-tailed test looks for any change. If the claim uses words like "increased" or "decreased", it is a one-tailed test, while terms like "change" or "different" indicate a two-tailed test. A one-tailed test has a critical region in one tail, while a two-tailed test has critical regions in both tails. The example given is a one-tailed test comparing civil engineering and B.A. salaries, with the alternative hypothesis that engineering salaries are greater than B.A. salaries.

Gerstman_PP09.ppt

This document provides an overview of hypothesis testing and the steps involved. It discusses:
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2) Calculating the test statistic, which is used to test the null hypothesis. For a one-sample z-test, this involves calculating the z-score when the population standard deviation is known.
3) Computing the p-value, which is the probability of observing a test statistic as extreme or more extreme than what was observed, assuming the null hypothesis is true. Small p-values provide strong evidence against the null.
4) Interpre

decimals-comp-packet.pdf

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This document provides an overview of hypothesis testing. It begins with an outline of the key topics to be covered, including the logic of hypothesis testing, types of errors, specific hypothesis tests, effect size, and statistical power. The body of the document then defines these concepts in more detail through examples and explanations. It discusses how to state hypotheses, set decision criteria, collect and analyze data, and make conclusions regarding whether to reject the null hypothesis. Factors that influence hypothesis tests like sample characteristics and test assumptions are also outlined.

sesion1.ppt

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uebersicht.ppt

uebersicht.ppt

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erzwungen.ppt

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harmonisch.ppt

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decimals-comp-packet.pdf

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- 1. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. Hypothesis Testing – Examples and Case Studies Chapter 23
- 2. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. 2 23.1 How Hypothesis Tests Are Reported in the News 1. Determine the null hypothesis and the alternative hypothesis. 2. Collect and summarize the data into a test statistic. 3. Use the test statistic to determine the p-value. 4. The result is statistically significant if the p-value is less than or equal to the level of significance. Often media only presents results of step 4.
- 3. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. 3 23.2 Testing Hypotheses About Proportions and Means If the null and alternative hypotheses are expressed in terms of a population proportion, mean, or difference between two means and if the sample sizes are large … … the test statistic is simply the corresponding standardized score computed assuming the null hypothesis is true; and the p-value is found from a table of percentiles for standardized scores.
- 4. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. 4 Example 2: Weight Loss for Diet vs Exercise Diet Only: sample mean = 5.9 kg sample standard deviation = 4.1 kg sample size = n = 42 standard error = SEM1 = 4.1/ √42 = 0.633 Exercise Only: sample mean = 4.1 kg sample standard deviation = 3.7 kg sample size = n = 47 standard error = SEM2 = 3.7/ √47 = 0.540 Did dieters lose more fat than the exercisers? measure of variability = [(0.633)2 + (0.540)2] = 0.83
- 5. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. 5 Example 2: Weight Loss for Diet vs Exercise The sample mean difference = 5.9 – 4.1 = 1.8 kg and the standard error of the difference is 0.83. Step 1. Determine the null and alternative hypotheses. Null hypothesis: No difference in average fat lost in population for two methods. Population mean difference is zero. Alternative hypothesis: There is a difference in average fat lost in population for two methods. Population mean difference is not zero. Step 2. Collect and summarize data into a test statistic. So the test statistic: z = 1.8 – 0 = 2.17 0.83
- 6. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. 6 Example 2: Weight Loss for Diet vs Exercise Step 3. Determine the p-value. Recall the alternative hypothesis was two-sided. p-value = 2 × [proportion of bell-shaped curve above 2.17] Table 8.1 => proportion is about 2 × 0.015 = 0.03. Step 4. Make a decision. The p-value of 0.03 is less than or equal to 0.05, so … • If really no difference between dieting and exercise as fat loss methods, would see such an extreme result only 3% of the time, or 3 times out of 100. • Prefer to believe truth does not lie with null hypothesis. We conclude that there is a statistically significant difference between average fat loss for the two methods.
- 7. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. 7 Example 3: Public Opinion About President On May 16, 1994, Newsweek reported the results of a public opinion poll that asked: “From everything you know about Bill Clinton, does he have the honesty and integrity you expect in a president?” (p. 23). Poll surveyed 518 adults and 233, or 0.45 of them (clearly less than half), answered yes. Could Clinton’s adversaries conclude from this that only a minority (less than half) of the population of Americans thought Clinton had the honesty and integrity to be president?
- 8. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. 8 Example 3: Public Opinion About President Step 1. Determine the null and alternative hypotheses. Null hypothesis: There is no clear winning opinion on this issue; the proportions who would answer yes or no are each 0.50. Alternative hypothesis: Fewer than 0.50, or 50%, of the population would answer yes to this question. The majority do not think Clinton has the honesty and integrity to be president. Step 2. Collect and summarize data into a test statistic. Sample proportion is: 233/518 = 0.45. The standard deviation = (0.50) × (1 – 0.50) = 0.022. 518 Test statistic: z = (0.45 – 0.50)/0.022 = –2.27
- 9. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. 9 Example 3: Public Opinion About President Step 3. Determine the p-value. Recall the alternative hypothesis was one-sided. p-value = proportion of bell-shaped curve below –2.27 Exact p-value = 0.0116. Step 4. Make a decision. The p-value of 0.0116 is less than 0.05, so we conclude that the proportion of American adults in 1994 who believed Bill Clinton had the honesty and integrity they expected in a president was significantly less than a majority.
- 10. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. 10 23.3 Revisiting Case Studies: How Journals Present Tests Whereas newspapers and magazines tend to simply report the decision from hypothesis testing, journals tend to report p-values as well. This allows you to make your own decision, based on the severity of a type 1 error and the magnitude of the p-value.
- 11. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. 11 Case Study 5.1: Quitting Smoking with Nicotine Patches Compared the smoking cessation rates for smokers randomly assigned to use a nicotine patch versus a placebo patch. Null hypothesis: The proportion of smokers in the population who would quit smoking using a nicotine patch and a placebo patch are the same. Alternative hypothesis: The proportion of smokers in the population who would quit smoking using a nicotine patch is higher than the proportion who would quit using a placebo patch.
- 12. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. 12 Case Study 5.1: Quitting Smoking with Nicotine Patches Higher smoking cessation rates were observed in the active nicotine patch group at 8 weeks (46.7% vs 20%) (P < .001) and at 1 year (27.5% vs 14.2%) (P = .011). (Hurt et al., 1994, p. 595) Conclusion: p-values are quite small: less than 0.001 for difference after 8 weeks and equal to 0.011 for difference after a year. Therefore, rates of quitting are significantly higher using a nicotine patch than using a placebo patch after 8 weeks and after 1 year.
- 13. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. 13 Case Study 6.4: Smoking During Pregnancy and Child’s IQ Study investigated impact of maternal smoking on subsequent IQ of child at ages 1, 2, 3, and 4 years of age. Null hypothesis: Mean IQ scores for children whose mothers smoke 10 or more cigarettes a day during pregnancy are same as mean for those whose mothers do not smoke, in populations similar to one from which this sample was drawn. Alternative hypothesis: Mean IQ scores for children whose mothers smoke 10 or more cigarettes a day during pregnancy are not the same as mean for those whose mothers do not smoke, in populations similar to one from which this sample was drawn.
- 14. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. 14 Case Study 6.4: Smoking During Pregnancy and Child’s IQ Children born to women who smoked 10+ cigarettes per day during pregnancy had developmental quotients at 12 and 24 months of age that were 6.97 points lower (averaged across these two time points) than children born to women who did not smoke during pregnancy (95% CI: 1.62,12.31, P = .01); at 36 and 48 months they were 9.44 points lower (95% CI: 4.52, 14.35, P = .0002). (Olds et al., 1994, p. 223) Researchers conducted two-tailed tests for possibility the mean IQ score could actually be higher for those whose mothers smoke. The CI provides evidence of the direction in which the difference falls. The p-value simply tells us there is a statistically significant difference.
- 15. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. 15 For Those Who Like Formulas
- 16. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. 16 For Those Who Like Formulas
- 17. Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. 17 For Those Who Like Formulas