Student's t-test is used to determine if two population means are statistically different based on random samples from those populations. It calculates a ratio of the difference between sample means to the variability within each sample. If the t-value is large enough based on the sample sizes and pre-set significance level (often 0.05), then the population means are considered statistically different. The t-test is commonly used to compare outcomes before and after an intervention or between treated and control groups.
Introduction to biostatistics and its application in various sectors.
Introduction to variables and variation.
Different types of variables and their introduction.
Use of biostatistics in various fields.
Cross over design, Placebo and blinding techniques Dinesh Gangoda
A crossover design is a modified randomized block design in which each block receives more than one treatment at different dosing periods.
A block can be a patient or a group of patients.
Patients in each block receive different sequences of treatments.
A crossover design is called a complete crossover design if each sequence contains all treatments under investigation.
A placebo is a dummy medicine containing no active substance.
This substance has no therapeutic effect, used as a control in testing new drugs.
Latin- ‘ I shall please’
Introduction to biostatistics and its application in various sectors.
Introduction to variables and variation.
Different types of variables and their introduction.
Use of biostatistics in various fields.
Cross over design, Placebo and blinding techniques Dinesh Gangoda
A crossover design is a modified randomized block design in which each block receives more than one treatment at different dosing periods.
A block can be a patient or a group of patients.
Patients in each block receive different sequences of treatments.
A crossover design is called a complete crossover design if each sequence contains all treatments under investigation.
A placebo is a dummy medicine containing no active substance.
This substance has no therapeutic effect, used as a control in testing new drugs.
Latin- ‘ I shall please’
Today’s overwhelming number of techniques applicable to data analysis makes it extremely difficult to define the most beneficial approach while considering all the significant variables.
The analysis of variance has been studied from several approaches, the most common of which uses a linear model that relates the response to the treatments and blocks. Note that the model is linear in parameters but may be nonlinear across factor levels. Interpretation is easy when data is balanced across factors but much deeper understanding is needed for unbalanced data.
Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means. In other words, the ANOVA is used to test the difference between two or more means.Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study.
Sir Ronald Fisher pioneered the development of ANOVA for analyzing results of agricultural experiments.1 Today, ANOVA is included in almost every statistical package, which makes it accessible to investigators in all experimental sciences. It is easy to input a data set and run a simple ANOVA, but it is challenging to choose the appropriate ANOVA for different experimental designs, to examine whether data adhere to the modeling assumptions, and to interpret the results correctly. The purpose of this report, together with the next 2 articles in the Statistical Primer for Cardiovascular Research series, is to enhance understanding of ANVOA and to promote its successful use in experimental cardiovascular research. My colleagues and I attempt to accomplish those goals through examples and explanation, while keeping within reason the burden of notation, technical jargon, and mathematical equations.
Parametric and non parametric test in biostatistics Mero Eye
This ppt will helpful for optometrist where and when to use biostatistic formula along with different examples
- it contains all test on parametric or non-parametric test
Introduction to Higuchi plots for tablet dissolution
Dissolution, Dissolution Models, Higuchi Plot
Presented by
Mohamed Omar Mahmoud
Department of Pharmaceutics
Researchers, as a whole, tend to underestimate the need for power. I'm just now starting to get it.
I recently gave a brief, easy-to-follow presentation on statistical power, it's importance, and how to go about getting it.
Hope you find it useful.
when you can measure what you are speaking about and express it in numbers, you know something about it but when you cannot measure, when you cannot express it in numbers, your knowledge is of meagre and unsatisfactory kind.”
Today’s overwhelming number of techniques applicable to data analysis makes it extremely difficult to define the most beneficial approach while considering all the significant variables.
The analysis of variance has been studied from several approaches, the most common of which uses a linear model that relates the response to the treatments and blocks. Note that the model is linear in parameters but may be nonlinear across factor levels. Interpretation is easy when data is balanced across factors but much deeper understanding is needed for unbalanced data.
Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means. In other words, the ANOVA is used to test the difference between two or more means.Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study.
Sir Ronald Fisher pioneered the development of ANOVA for analyzing results of agricultural experiments.1 Today, ANOVA is included in almost every statistical package, which makes it accessible to investigators in all experimental sciences. It is easy to input a data set and run a simple ANOVA, but it is challenging to choose the appropriate ANOVA for different experimental designs, to examine whether data adhere to the modeling assumptions, and to interpret the results correctly. The purpose of this report, together with the next 2 articles in the Statistical Primer for Cardiovascular Research series, is to enhance understanding of ANVOA and to promote its successful use in experimental cardiovascular research. My colleagues and I attempt to accomplish those goals through examples and explanation, while keeping within reason the burden of notation, technical jargon, and mathematical equations.
Parametric and non parametric test in biostatistics Mero Eye
This ppt will helpful for optometrist where and when to use biostatistic formula along with different examples
- it contains all test on parametric or non-parametric test
Introduction to Higuchi plots for tablet dissolution
Dissolution, Dissolution Models, Higuchi Plot
Presented by
Mohamed Omar Mahmoud
Department of Pharmaceutics
Researchers, as a whole, tend to underestimate the need for power. I'm just now starting to get it.
I recently gave a brief, easy-to-follow presentation on statistical power, it's importance, and how to go about getting it.
Hope you find it useful.
when you can measure what you are speaking about and express it in numbers, you know something about it but when you cannot measure, when you cannot express it in numbers, your knowledge is of meagre and unsatisfactory kind.”
T test, Student’s t Test, Key Takeaways, Uses of t-test / Application , Type of t-test, Type of t-test Cont.., One-tailed or two-tailed t-test, Which t-test to Use, t-test Formula, The t-score, Understanding P-values, Degrees of Freedom, How is the t-distribution table used, Example, Example Cont.., Different t-test Formulae, Different t-test Formulae Cont.., Reference.
Inferential Analysis
Chapter 20
NUR 6812Nursing Research
Florida National University
Introduction - Inferential Analysis
We will discuss analysis of variance and regression, which are technically part of the same family of statistics known as the general linear method but are used to achieve different analytical goals
ANALYSIS OF VARIANCE
Analysis of variance (ANOVA) is used so often that Iversen and Norpoth (1987) said they once had a student who thought this was the name of an Italian statistician.
You can think of analysis of variance as a whole family of procedures beginning with the simple and frequently used t-test and becoming quite complicated with the use of multiple dependent variables (MANOVA, to be explained later in this chapter) and covariates.
Although the simpler varieties of these statistics can actually be calculated by hand, it is assumed that you will use a statistical software package for your calculations.
If you want to see how these calculations are done, you could try to compute a correlation, chi-square, t-test, or ANOVA yourself (see Yuker, 1958; Field, 2009), but in general it is too time consuming and too subject to human error to do these by hand.
IMPORTANT TERMINOLOGY
Several terms are used in these analyses that you need to be familiar with to understand the analyses themselves and the results. Many will already be familiar to you.
Statistical significance: This indicates the probability that the differences found are a result of error, not the treatment. Stated in terms of the P value, the convention is to accept either a 1% (P ≤ 0.01), or 1 out of 100, or 5% (P ≤ 0.05), or 5 out of 100, possibility that any differences seen could have been due to error (Cortina & Dunlap, 2007).
Research hypothesis: A research hypothesis is a declarative statement of the expected relationship between the dependent and independent variable(s).
Null hypothesis: The null hypothesis, based on the research hypothesis, states that the predicted relationships will not be found or that those found could have occurred by chance, meaning the difference will not be statistically significant.
Effect size: This is defined by Cortina and Dunlap as “the amount of variance in one variable accounted for by another in the sample at hand” (2007, p. 231). Effect size estimates are helpful adjuncts to significance testing. An important limitation, however, is that they are heavily influenced by the type of treatment or manipulation that occurred and the measures that are used.
Confidence intervals: Although sometimes suggested as an adjunct or replacement for the significance level, confidence intervals are determined in part by the alpha (significance level) (Cortina & Dunlap, 2007). Likened to a margin of error, the confidence intervals indicate the range within which the true difference between means may lie. A narrow confidence interval implies high precision; we can specify believable values within a narrow range ...
Inferential Analysis
Chapter 20
NUR 6812Nursing Research
Florida National University
Introduction - Inferential Analysis
We will discuss analysis of variance and regression, which are technically part of the same family of statistics known as the general linear method but are used to achieve different analytical goals
ANALYSIS OF VARIANCE
Analysis of variance (ANOVA) is used so often that Iversen and Norpoth (1987) said they once had a student who thought this was the name of an Italian statistician.
You can think of analysis of variance as a whole family of procedures beginning with the simple and frequently used t-test and becoming quite complicated with the use of multiple dependent variables (MANOVA, to be explained later in this chapter) and covariates.
Although the simpler varieties of these statistics can actually be calculated by hand, it is assumed that you will use a statistical software package for your calculations.
If you want to see how these calculations are done, you could try to compute a correlation, chi-square, t-test, or ANOVA yourself (see Yuker, 1958; Field, 2009), but in general it is too time consuming and too subject to human error to do these by hand.
IMPORTANT TERMINOLOGY
Several terms are used in these analyses that you need to be familiar with to understand the analyses themselves and the results. Many will already be familiar to you.
Statistical significance: This indicates the probability that the differences found are a result of error, not the treatment. Stated in terms of the P value, the convention is to accept either a 1% (P ≤ 0.01), or 1 out of 100, or 5% (P ≤ 0.05), or 5 out of 100, possibility that any differences seen could have been due to error (Cortina & Dunlap, 2007).
Research hypothesis: A research hypothesis is a declarative statement of the expected relationship between the dependent and independent variable(s).
Null hypothesis: The null hypothesis, based on the research hypothesis, states that the predicted relationships will not be found or that those found could have occurred by chance, meaning the difference will not be statistically significant.
Effect size: This is defined by Cortina and Dunlap as “the amount of variance in one variable accounted for by another in the sample at hand” (2007, p. 231). Effect size estimates are helpful adjuncts to significance testing. An important limitation, however, is that they are heavily influenced by the type of treatment or manipulation that occurred and the measures that are used.
Confidence intervals: Although sometimes suggested as an adjunct or replacement for the significance level, confidence intervals are determined in part by the alpha (significance level) (Cortina & Dunlap, 2007). Likened to a margin of error, the confidence intervals indicate the range within which the true difference between means may lie. A narrow confidence interval implies high precision; we can specify believable values within a narrow range ...
Statistics What you Need to KnowIntroductionOften, when peop.docxdessiechisomjj4
Statistics: What you Need to Know
Introduction
Often, when people begin a statistics course, they worry about doing advanced mathematics or their math phobias kick in. Understanding that statistics as addressed in this course is not a math course at all is important. The only math you will do is addition, subtraction, multiplication, and division. In these days of computer capability, you generally don't even have to do that much, since Excel is set up to do basic statistics for you. The key elements for the student in this course is to understand the various types of statistics, what their requirements are, what they do, and how you can use and interpret the results. Referring back to the basic components of a valid research study, which statistic a researcher uses depends on several things:
The research question itself
The sample size
The type of data you have collected
The type of statistic called for by the design
All quantitative studies require a data set. Qualitative studies may use a data set or may use observations with no numerical data at all. For the purposes of the next modules, our focus will be on quantitative studies.
Types of Statistics
There are several types of statistics available to the researcher. Descriptive statistics provide a basic description of the data set. This includes the measures of central tendency: means, medians, and modes, and the measures of dispersion, including variances and standard deviations. Descriptive statistics also include the sample size, or "N", and the frequency with which each data point occurs in the data set.
Inferential statistics allow the researcher to make predictions, estimations, and generalizations about the data set, the sample, and the population from which the sample was drawn. They allow you to draw inferences, generalizations, and possibilities regarding the relationship between the independent variable and the dependent variable to indicate how those inferences answer the research question. Researchers can make predictions and estimations about how the results will fit the overall population. Statistics can also be described in terms of the types of data they can analyze. Non-parametric statistics can be used with nominal or ordinal data, while parametric statistics can be used with interval and ratio data types.
Types of Data
There are four types of data that a researcher may collect.
Nominal Data Sets
The Nominal data set includes simple classifications of data into categories which are all of equal weight and value. Examples of categories that are equal to each other include gender (male, female), state of birth (Arizona, Wyoming, etc.), membership in a group (yes, no). Each of these categories is equivalent to the other, without value judgments.
Ordinal Data Sets
Ordinal data sets also have data classified into categories, but these categories have some form or order or ranking attached, often of some sort of value / val.
Exploring Career Paths in Cybersecurity for Technical CommunicatorsBen Woelk, CISSP, CPTC
Brief overview of career options in cybersecurity for technical communicators. Includes discussion of my career path, certification options, NICE and NIST resources.
Want to move your career forward? Looking to build your leadership skills while helping others learn, grow, and improve their skills? Seeking someone who can guide you in achieving these goals?
You can accomplish this through a mentoring partnership. Learn more about the PMISSC Mentoring Program, where you’ll discover the incredible benefits of becoming a mentor or mentee. This program is designed to foster professional growth, enhance skills, and build a strong network within the project management community. Whether you're looking to share your expertise or seeking guidance to advance your career, the PMI Mentoring Program offers valuable opportunities for personal and professional development.
Watch this to learn:
* Overview of the PMISSC Mentoring Program: Mission, vision, and objectives.
* Benefits for Volunteer Mentors: Professional development, networking, personal satisfaction, and recognition.
* Advantages for Mentees: Career advancement, skill development, networking, and confidence building.
* Program Structure and Expectations: Mentor-mentee matching process, program phases, and time commitment.
* Success Stories and Testimonials: Inspiring examples from past participants.
* How to Get Involved: Steps to participate and resources available for support throughout the program.
Learn how you can make a difference in the project management community and take the next step in your professional journey.
About Hector Del Castillo
Hector is VP of Professional Development at the PMI Silver Spring Chapter, and CEO of Bold PM. He's a mid-market growth product executive and changemaker. He works with mid-market product-driven software executives to solve their biggest growth problems. He scales product growth, optimizes ops and builds loyal customers. He has reduced customer churn 33%, and boosted sales 47% for clients. He makes a significant impact by building and launching world-changing AI-powered products. If you're looking for an engaging and inspiring speaker to spark creativity and innovation within your organization, set up an appointment to discuss your specific needs and identify a suitable topic to inspire your audience at your next corporate conference, symposium, executive summit, or planning retreat.
About PMI Silver Spring Chapter
We are a branch of the Project Management Institute. We offer a platform for project management professionals in Silver Spring, MD, and the DC/Baltimore metro area. Monthly meetings facilitate networking, knowledge sharing, and professional development. For event details, visit pmissc.org.
1. Student’s t Test
Ibrahim A. Alsarra, Ph.D.
King Saud University
College of Pharmacy
Department of Pharmaceutics
PHT 463: Quality Control of Pharmaceutical Dosage Forms
Summer Semester of 1423-1424H
2. Statistical Estimation and Hypothesis
Testing
Parameters estimates from samples are meant to be used
to estimate the true population parameters.
The sample mean and variance are typical estimators or
predictors of the true mean and variance.
3. Introduction
"Student" (real name: W. S. Gossett [1876-1937])
developed statistical methods to solve problems
stemming from his employment in a brewery.
Student's t-test deals with the problems associated
with inference based on "small" samples: the
calculated mean and standard deviation may by
chance deviate from the "real" mean and standard
deviation (i.e., what you'd measure if you had many
more data items: a "large" sample).
4. Introduction
'Student's' t Test is one of the most
commonly used techniques for testing a
hypothesis on the basis of a difference
between sample means. Explained in
layman's terms, the t test determines a
probability that two populations are the
same with respect to the variable tested.
5. Criteria
If there is a direct relationship between specific data
points from two data sets, such as measurements on the
same subject 'before and after,' then the paired t test MAY
be appropriate.
If samples are collected randomly from two different
populations or from different individuals within the same
population at different times, use the test for independent
samples (unpaired).
Here's a simple way to determine if the paired t test can
apply - if one sample can have a different number of data
points from the other, then the paired t test cannot apply.
6. 'Student's' t Test (For Paired
Samples)
The number of points in each data set must be
the same, and they must be organized in pairs,
in which there is a definite relationship
between each pair of data points
If the data were taken as random samples, you
must use the independent test even if the
number of data points in each set is the same
Even if data are related in pairs, sometimes the
paired t is still inappropriate
7. Example
The paired t test is generally used when measurements
are taken from the same subject before and after some
manipulation such as injection of a drug. For example,
you can use a paired t test to determine the significance
of a difference in blood pressure before and after
administration of an experimental pressure substance.
You can also use a paired t test to compare samples that
are subjected to different conditions, provided the
samples in each pair are identical otherwise. For
example, you might test the effectiveness of a water
additive in reducing bacterial numbers by sampling water
from different sources and comparing bacterial counts in
the treated versus untreated water sample. Each different
water source would give a different pair of data points.
8. Remember that…
The t-test assesses whether the means of two groups
are statistically different from each other.
This analysis is appropriate whenever you want to
compare the means of two groups
9. Figure 1. Idealized distributions for
treated and comparison group post-
test values
Figure 1 shows the
distributions for the treated
(blue) and control (green)
groups in a study. Actually,
the figure shows the
idealized distribution -- the
actual distribution would
usually be depicted with a
histogram or bar graph. The
figure indicates where the
control and treatment group
means are located. The
question the t-test
addresses is whether the
means are statistically
different.
10. What does it mean to say that the averages
for two groups are statistically different?
11. Figure 2. Three scenarios for differences
between means
Consider the three situations
shown in Figure 2. The first
thing to notice about the
three situations is that the
difference between the
means is the same in all
three. But, you should also
notice that the three
situations don't look the
same -- they tell very
different stories. The top
example shows a case with
moderate variability of
scores within each group.
12. Figure 2. Three scenarios for
differences between means …cont.
The second situation shows the
high variability case. the third
shows the case with low variability.
Clearly, we would conclude that the
two groups appear most different
or distinct in the bottom or low-
variability case. Why? Because
there is relatively little overlap
between the two bell-shaped
curves. In the high variability case,
the group difference appears least
striking because the two bell-
shaped distributions overlap so
much.
13. This leads us to a very important
conclusion: when we are looking at the
differences between scores for two groups,
we have to judge the difference between
their means relative to the spread or
variability of their scores. The t-test does
just this.
14. Statistical Analysis of the t-test
The formula for the t-test is a ratio. The top
part of the ratio is just the difference between
the two means or averages. The bottom part is
a measure of the variability or dispersion of the
scores. This formula is essentially another
example of the signal-to-noise metaphor in
research: the difference between the means is
the signal that, in this case, we think our
program or treatment introduced into the data;
the bottom part of the formula is a measure of
variability that is essentially noise that may
make it harder to see the group difference
15. Figure 3 shows the formula for the t-test and how the
numerator and denominator are related to the
distributions
16. The top part of the formula is easy to compute -- just find
the difference between the means. The bottom part is
called the standard error of the difference. To compute
it, we take the variance for each group and divide it by the
number of people in that group. We add these two values
and then take their square root. The specific formula is
given in Figure 4:
Figure 4. Formula for the Standard error of
the difference between the means.
17. Remember, that the variance is simply the
square of the standard deviation.
The final formula for the t-test is shown in
Figure 5:
Figure 5. Formula for the t-test
18. Finally
The t-value will be positive if the first mean is larger
than the second and negative if it is smaller. Once
you compute the t-value you have to look it up in a
table of significance to test whether the ratio is large
enough to say that the difference between the
groups is not likely to have been a chance finding. To
test the significance, you need to set a risk level
(called the alpha level). In most social research, the
"rule of thumb" is to set the alpha level at .05.
19. Finally
This means that five times out of a hundred you
would find a statistically significant difference
between the means even if there was none (i.e., by
"chance"). You also need to determine the degrees
of freedom (df) for the test. In the t-test, the degrees
of freedom is the sum of the persons in both groups
minus 2. Given the alpha level, the df, and the t-
value, you can look the t-value up in a standard table
of significance (available as an appendix in the back
of most statistics texts) to determine whether the t-
value is large enough to be significant.
20. Finally
If it is, you can conclude that the difference between
the means for the two groups is different (even given
the variability). Fortunately, statistical computer
programs routinely print the significance test results
and save you the trouble of looking them up in a
table.