Notes on Biostatistic principles

1. Comparison of Means of Two Samples: The t-test. The t-test, developed by William Gossett, compares means of two
independent samples or paired observations. It is crucial for comparing groups in studies like clinical trials or
experiments.
The Mann-Whitney U test and Median Test are alternatives for non-interval data analysis, focusing on rank-ordering and
medians for statistical comparisons.
Statistical inference involves assuming no difference between groups, determining probabilities of observed differences
by chance, and making conclusions based on significance levels like p<0.05.
The Central Limit Theorem states that for sufficiently large sample sizes, means will be normally distributed, allowing the
use of parametric statistics for inferences about mean values.
Statistical Inference Practice Question: What is the purpose of statistical inference in research studies? [6](#6).
Model Answer: Statistical inference aims to assess the likelihood of generalizing observed results to other samples,
helping researchers determine if differences between groups could have occurred by chance beyond the experimental
intervention's impact.
Comparison of Means of Two Samples The t-test Practice Question: Who developed the t-test and what is its significance
in research studies? [37](#37).
Model Answer: The t-test was developed by William Gossett and is crucial for comparing means of two independent
samples or paired observations in studies like clinical trials or experiments.
Flashcards Question: What is the Mann-Whitney U test used for in statistical analysis? [82](#82).
Flashcards Answer: The Mann-Whitney U test is used to compare the relative ranks of subjects in two groups, focusing
on rank-ordering data for statistical comparisons.
Descriptive Statistics Flashcard Question: What is the main goal of descriptive statistics? 6.
Answer: The main goal of descriptive statistics is to analyze data to communicate study results by determining average
values, ranges, and other characteristics without generalizing beyond the sample studied.
Statistical Inference Flashcard Question: What is the purpose of statistical inference in research studies? 6.
Answer: Statistical inference aims to assess the likelihood of generalizing observed results to other samples, helping
researchers determine if differences between groups could have occurred by chance beyond the experimental
intervention's impact.
Comparison of Means of Two Samples The t-test Flashcard Question: Who developed the t-test and why is it significant
in research studies? 37.
Answer: The t-test was developed by William Gossett and is essential for comparing means of two independent samples
or paired observations in studies like clinical trials or experiments.
Flashcards Question: What is the Mann-Whitney U test used for in statistical analysis? 82.
Flashcards Answer: The Mann-Whitney U test is utilized to compare the relative ranks of subjects in two groups,
focusing on rank-ordering data for statistical comparisons.

2. ------------------
dugkosasan:
Explain in simplified detailed model answer ANOVA Linear regression and multiple linear regression and their
importance, differences and similarities in terms that a 16-year-old would understand
undefined:
Linear regression is like drawing a straight line through points on a graph to see how one thing changes when another
thing changes. Multiple linear regression is like drawing more complex lines with different slopes to understand how
several things together affect something else. ANOVA is used to compare averages between groups, while ANCOVA is a
simpler version of regression. Hierarchical linear models help study changes over time in groups or individuals by looking
at different levels of influence, like how schools affect teachers and teachers affect students. Logistic regression is used
when predicting outcomes with only two possibilities, like alive or dead, by constraining answers between 0 and 1.
[55](#55) [49](#49) [57](#57)
dugkosasan:
Explain Comparison among Many means Analysis of Variance, FACTORIAL ANOVA and REPEATED MEASURES ANOVA ,
their differences, similarities and examples in terms that a 16-year-old would understand
undefined:
Analysis of Variance (ANOVA) is like comparing different groups to see if they are really different, like testing if various
cold medicines work differently. Factorial ANOVA is about looking at how multiple factors, like types of cold symptoms
and different medicines, interact to see which works best for each. Repeated Measures ANOVA is when you measure
the same thing multiple times on the same person, like checking how their pain changes over time with different
treatments. Each helps us understand how things vary and interact in experiments, like comparing different brands of
products or testing how people respond to different treatments over time. [28](#28) [33](#33) [38](#38)
dugkosasan:
Explain in simplified detailed model answer regression analysis, and how to test significance and strength of relationship
in simple regression in terms that a 16-year-old would understand
undefined:
Regression analysis is like drawing a line on a graph to see how one thing changes when another thing changes, like how
skinfold thickness affects blood sugar levels. To test the strength of the relationship, we look at how closely the data
points follow the line - the closer they are, the stronger the relationship. Significance testing checks if the relationship
we see is real or just random chance. It's like making sure the pattern we found is not just a coincidence. By doing this,
we can understand how well one thing predicts another and how confident we can be in our predictions. [41](#41)
dugkosasan:
Explain in simplified detailed model answer Pearson correlation coefficient, multiple correlation coefficient,
standardized and unstandardized regression coefficients, and they influence strength of relationship in regression in
terms that a 16-year-old would understand
undefined:
In simple terms, the Pearson correlation coefficient is like a number that tells us how closely two things are related on a
graph, such as how height and shoe size might be related. The multiple correlation coefficient is similar, but it looks at
how several things together affect something else, like how diet, exercise, and sleep impact overall health. Standardized
regression coefficients are like comparing apples to oranges by making them all the same size, so we can see which one
is more important in predicting something. Unstandardized regression coefficients are like looking at the actual numbers

3. without changing their sizes. These coefficients help us understand how strong the relationship is between different
factors in a prediction model. [43](#43)
dugkosasan:
Explain in simplified detailed model answer the calculation of Pearson correlation coefficient from Sum of Squares
regression and Sum of Squares residual, and how it influences significance of relationship in terms that a 16-year-old
would understand
undefined:
To calculate the Pearson correlation coefficient, we use the Sum of Squares (SS) from regression and residual. The
correlation coefficient shows how closely two things are related on a graph, like how studying time affects exam scores.
The SS from regression measures how well the line fits the data points, while the SS residual shows how much the data
points vary from the line. A higher correlation coefficient means a stronger relationship between the variables, like how
studying more usually leads to higher exam scores. So, by looking at these values, we can understand how well one thing
predicts another and how confident we can be in our predictions. [43](#43)
dugkosasan:
Explain in simplified detailed model answer the different variations on Linear Regression such as Logistic regression,
General Linear Model, and Hierarchical Linear Models and their importance in research in terms that a 16-year-old
would understand
undefined:
Linear regression is like drawing a straight line on a graph to see how one thing changes when another thing changes,
such as how studying time affects exam scores. Multiple linear regression is more complex, using different lines with
various slopes to understand how several things together affect something else, like how diet, exercise, and sleep
impact overall health. Logistic regression is used when predicting outcomes with only two possibilities, like alive or dead,
by constraining answers between 0 and 1. The General Linear Model allows for both continuous and categorical
independent variables, while Hierarchical Linear Models help study changes over time in groups or individuals by looking
at different levels of influence, like how schools affect teachers and teachers affect students. [55](#55) [49](#49)
[57](#57)
dugkosasan:
Explain in simplified detailed model answer which are the Nonparametric statistical tests and describe briefly what each
test entails in terms that a 16-year-old would understand
dugkosasan:
Explain in simplified detailed model answer the chi-square test, the binominal test, Fisher’s exact test, the
McNemar chi-square test, the Mann-Whitney U test, median test,
Kruskal-Wallis test, Kolmogorov-Smirnov test, the sign test and Wilcoxon test in terms that a 16-year-old would
understand
undefined:
The chi-square test is like a detective checking if two things are related by comparing expected and observed numbers in
categories, such as checking if more boys play video games than girls. The binomial test is for comparing two groups, like
testing if a new medicine helps more patients survive than an old one. Fisher’s exact test is for small groups, like
comparing the success rates of two different study methods with only a few students. The McNemar chi-square test is
for before-and-after data, like seeing if more people prefer a new phone after trying it. The Mann-Whitney U test
compares two groups' rankings, like checking if boys or girls score higher in a test. The median test looks at the middle
value in data sets to see if they differ, like comparing the average ages of students in two classes. The Kruskal-Wallis test
is for comparing more than two groups, like checking if students from different grades have different average heights.
The Kolmogorov-Smirnov test compares two groups' distributions, like seeing if the heights of boys and girls follow the
same pattern. The sign test checks if there is a difference before and after an event, like checking if students' grades

4. improved after extra tutoring. The Wilcoxon test compares two related groups, like checking if students' scores
increased after a study session. [61](#61)
dugkosasan:
Explain in simplified detailed model answer why correlation is not the same as causation in terms that a 16-year-old
would understand
undefined:
Correlation is not the same as causation because just because two things are related doesn't mean one causes the other.
For example, height and weight are highly correlated, but being tall doesn't cause someone to weigh more. Researchers
can misinterpret strong correlations as evidence of a cause-and-effect relationship when other factors may be at play.
So, correlation shows a connection between variables, but it doesn't prove that one thing directly causes the other.
[47](#47).
dugkosasan:
Explain in simplified detailed model answer regression with categorical independent variables and regression with
interaction terms, their differences and importance in terms that a 16-year-old would understand
undefined:
Regression with categorical independent variables involves using categories like treatment types to predict outcomes,
ensuring predictions are between 0 and 1, as seen in logistic regression. On the other hand, regression with interaction
terms examines how the relationship between variables changes based on another variable, allowing for a deeper
understanding of how factors interact. Both methods are crucial for understanding how different factors impact
outcomes and how they influence each other. Logistic regression is used when predicting binary outcomes, while
interaction terms help capture complex relationships in data. [49](#49), [53](#53).
------------------
dugkosasan:
Explain in simplified detailed model answer transformations before regression with categorical independent variables
and regression with interaction terms, their differences and importance in terms that a 16-year-old would understand-+
undefined:
Before regression with categorical independent variables, we use logistic regression to predict a binary outcome like
dead or alive, constraining predictions between 0 and 1 for interpretability. Logistic regression is essential when
predicting categorical outcomes with two states. On the other hand, regression with interaction terms involves creating
dummy variables to represent different groups, allowing for the comparison of treatment effects. Interaction terms are
crucial for understanding how the effect of one variable on the outcome changes based on the level of another variable.
In simpler terms, logistic regression helps predict categories like alive or dead, while regression with interaction terms
helps understand how different factors interact to influence outcomes. [49](#49) [53](#53).
dugkosasan:
Explain in simplified detailed model answer transformations before regression, regression assumptions and regression
plots, their differences and importance in terms that a 16-year-old would understand
undefined:
Before regression, we transform data to predict outcomes accurately. Regression assumptions ensure our predictions
are reliable, assuming relationships between variables. Regression plots help visualize data patterns. Transformations
help prepare data for prediction, assumptions ensure accurate results, and plots make it easier to understand
relationships visually. In simpler terms, transformations are like preparing ingredients for a recipe, assumptions are like
following cooking instructions, and plots are like seeing how the dish looks before serving. Each step is important for
making sure our predictions are trustworthy and easy to understand. [57](#57)

5. dugkosasan:
Explain in simplified detailed model answer the follwing regarding logistic regression: the requirements for binary
logistic regression, threshold for clinical interpretation and confusion matrix in terms that a 16-year-old would
understand
undefined:
To use binary logistic regression, we predict outcomes like dead or alive by coding them as 0 or 1 to keep predictions
between 0 and 1 for clarity [49](#49). The threshold for clinical interpretation is set at 0.5: predictions below mean alive,
above mean dead [51](#51). A confusion matrix compares predicted outcomes with actual outcomes in a 2x2 table to
evaluate the model's performance [53](#53). In simpler terms, binary logistic regression helps predict outcomes like
alive or dead, the 0.5 threshold decides the final prediction, and the confusion matrix checks how well the predictions
match reality.
dugkosasan:
Explain in simplified detailed model answer what AUC and ROC in logistic regression are in terms that a 16-year-old
would understand
undefined:
In logistic regression, AUC and ROC are used to evaluate how well the model predicts outcomes. AUC (Area Under the
Curve) is like a grade for the model's performance, where higher AUC means better predictions [51](#51). ROC (Receiver
Operating Characteristic) is like a graph showing how well the model distinguishes between different outcomes by
plotting true positive rate against false positive rate [51](#51). In simpler terms, AUC is a score that tells us how good the
model is at predicting, and ROC is a visual representation showing how well the model can separate different outcomes.
dugkosasan:
Summarize and explain in simplified detailed model answer Statistical inference, samples and populations, systematic
and random error, the central limit theorem, inferences about mean values based on a single sample, type I and type II
Errors, Alpha-levels and Beta-levels and other rules of Statistical Etiquette, One-tailed tests and two-tailed tests,
Confidence intervals, and statistical versus clinical significance in terms that a 16-year-old would understand
undefined:
Statistical inference helps draw conclusions about a whole group based on a smaller sample [1](#1). Samples represent a
group we want to learn about, like testing a few students to understand a whole class [2](#2). Errors in measurements
can be due to consistent mistakes (systematic error) or random chance (random error) [2](#2). The central limit theorem
says that if we repeat an experiment many times, the average results will form a bell-shaped curve, even if the original
data didn't look that way [4](#4). When we estimate values based on one sample, we're making educated guesses about
the whole group [6](#6). Type I errors happen when we wrongly think there's an effect, while Type II errors occur when
we miss a real effect [8](#8). Alpha and beta levels help decide how confident we are in our conclusions, like setting
rules for how sure we need to be before making a claim [8](#8). One-tailed tests focus on one direction of change, while
two-tailed tests look for any kind of difference [8](#8). Confidence intervals show a range where we think the true value
lies, like saying a test score is between 70 and 80 with 95% certainty [15](#15). Statistical significance means results are
unlikely due to chance, while clinical significance means results are important in real-world terms, like a new medicine
actually making people healthier [1](#1).
------------------
20220691:
Summarize, simplify and explain the different test in this document
undefined:

6. A sample size is the number of animals or subjects needed for an experiment. It's important because a sample size that
is too small might not show the effect of interest, while a sample size that is too large can waste resources. The goal is to
have enough samples to detect an effect without using too many resources [4](#4).
20220691:
Explain the one mean and two mean t test in terms that a 16-year-old would understand
undefined:
In a one mean t-test, we check if something is different from a specific value. For example, we might want to know if the
average income of college freshmen is less than $20,000. We collect data and compare it to this value to see if it's
significantly different. In a two mean t-test, we compare two groups to see if they are different from each other. For
instance, we could compare the average caloric intake of men and women to see if there's a significant difference
between them [14](#14) [18](#18).
------------------
20220691:
Summarize, simplify and explain the different test in this document
undefined:
A sample size is the number of animals or subjects needed for an experiment. It's important because a sample size that
is too small might not show the effect of interest, while a sample size that is too large can waste resources. The goal is to
have enough samples to detect an effect without using too many resources [4](#4).
20220691:
Explain the one mean amd two mean t test in terms that a 16-year-old would understand
undefined:
In a one mean t-test, we check if something is different from a specific value. For example, we might want to know if the
average income of college freshmen is less than $20,000. We collect data and compare it to this value to see if it's
significantly different. In a two mean t-test, we compare two groups to see if they are different from each other. For
instance, we could compare the average caloric intake of men and women to see if there's a significant difference
between them [14](#14) [18](#18).
20220691:
Explain the pairedt t test and the difference with other tests such as one mean and two mean t test in terms that a 16-
year-old would understand
undefined:
In a paired t-test, we compare measurements from the same group before and after something happens, like checking if
heart rate is higher after exercise compared to before. This is different from a one mean t-test where we compare one
group to a specific value, like checking if college freshmen earn less than $20,000. In a two mean t-test, we compare two
separate groups, like comparing the average body temperature of women to men to see if there's a significant
difference between them [20](#20) [15](#15) [16](#16).
20220691:
Explain the one-way ANOVA test and its importance in terms that a 16-year-old would understand
undefined:
The one-way ANOVA test checks if there is a difference in means among multiple groups, like comparing the weight lost
after different surgeries or white blood cell counts with different medications. It's like comparing the average scores of
students in different classes to see if one class performs significantly better. This test is important because it helps us
understand if there are real differences between groups, like if one surgery option leads to more weight loss than others

7. or if one medication regime affects white blood cell counts differently. It's a way to make fair comparisons and draw
meaningful conclusions from data [26](#26).
------------------
20220691:
Summarize, simplify and explain the different test in this document
undefined:
A sample size is the number of animals or subjects needed for an experiment. It's important because a sample size that
is too small might not show the effect of interest, while a sample size that is too large can waste resources. The goal is to
have enough samples to detect an effect without using too many resources [4](#4).
20220691:
Explain the one mean amd two mean t test in terms that a 16-year-old would understand
undefined:
In a one mean t-test, we check if something is different from a specific value. For example, we might want to know if the
average income of college freshmen is less than $20,000. We collect data and compare it to this value to see if it's
significantly different. In a two mean t-test, we compare two groups to see if they are different from each other. For
instance, we could compare the average caloric intake of men and women to see if there's a significant difference
between them [14](#14) [18](#18).
20220691:
Explain the pairedt t test and the difference with other tests such as one mean and two mean t test in terms that a 16-
year-old would understand
undefined:
In a paired t-test, we compare measurements from the same group before and after something happens, like checking if
heart rate is higher after exercise compared to before. This is different from a one mean t-test where we compare one
group to a specific value, like checking if college freshmen earn less than $20,000. In a two mean t-test, we compare two
separate groups, like comparing the average body temperature of women to men to see if there's a significant
difference between them [20](#20) [15](#15) [16](#16).
20220691:
Explain the one-way ANOVA test and its importance in terms that a 16-year-old would understand
undefined:
The one-way ANOVA test checks if there is a difference in means among multiple groups, like comparing the weight lost
after different surgeries or white blood cell counts with different medications. It's like comparing the average scores of
students in different classes to see if one class performs significantly better. This test is important because it helps us
understand if there are real differences between groups, like if one surgery option leads to more weight loss than others
or if one medication regime affects white blood cell counts differently. It's a way to make fair comparisons and draw
meaningful conclusions from data [26](#26).
20220691:
Explain the single proportion test and the two proportions test and the difference with other tests in terms that a 16-
year-old would understand
undefined:
In a single proportion test, we check if one group's proportion is different from a specific value, like comparing the
cancer rate in North Dakota to the US average for males or females. This is different from a two proportions test where
we compare two groups to see if their proportions are different, such as comparing the passing rates of students taught

8. by different teachers. The single proportion test focuses on one group, while the two proportions test looks at
differences between two groups, like comparing boys and girls in a class to see if there's a significant difference in their
choices or outcomes [30](#30), [32](#32), [34](#34).
------------------
20220691:
Summarize, simplify and explain the different test in this document
undefined:
A sample size is the number of animals or subjects needed for an experiment. It's important because a sample size that
is too small might not show the effect of interest, while a sample size that is too large can waste resources. The goal is to
have enough samples to detect an effect without using too many resources [4](#4).
20220691:
Explain the one mean amd two mean t test in terms that a 16-year-old would understand
undefined:
In a one mean t-test, we check if something is different from a specific value. For example, we might want to know if the
average income of college freshmen is less than $20,000. We collect data and compare it to this value to see if it's
significantly different. In a two mean t-test, we compare two groups to see if they are different from each other. For
instance, we could compare the average caloric intake of men and women to see if there's a significant difference
between them [14](#14) [18](#18).
20220691:
Explain the pairedt t test and the difference with other tests such as one mean and two mean t test in terms that a 16-
year-old would understand
undefined:
In a paired t-test, we compare measurements from the same group before and after something happens, like checking if
heart rate is higher after exercise compared to before. This is different from a one mean t-test where we compare one
group to a specific value, like checking if college freshmen earn less than $20,000. In a two mean t-test, we compare two
separate groups, like comparing the average body temperature of women to men to see if there's a significant
difference between them [20](#20) [15](#15) [16](#16).
20220691:
Explain the one-way ANOVA test and its importance in terms that a 16-year-old would understand
undefined:
The one-way ANOVA test checks if there is a difference in means among multiple groups, like comparing the weight lost
after different surgeries or white blood cell counts with different medications. It's like comparing the average scores of
students in different classes to see if one class performs significantly better. This test is important because it helps us
understand if there are real differences between groups, like if one surgery option leads to more weight loss than others
or if one medication regime affects white blood cell counts differently. It's a way to make fair comparisons and draw
meaningful conclusions from data [26](#26).
20220691:
Explain the single proportion test and the two proportions test and the difference with other tests in terms that a 16-
year-old would understand
undefined:
In a single proportion test, we check if one group's proportion is different from a specific value, like comparing the
cancer rate in North Dakota to the US average for males or females. This is different from a two proportions test where

9. we compare two groups to see if their proportions are different, such as comparing the passing rates of students taught
by different teachers. The single proportion test focuses on one group, while the two proportions test looks at
differences between two groups, like comparing boys and girls in a class to see if there's a significant difference in their
choices or outcomes [30](#30), [32](#32), [34](#34).
20220691:
Explain the Chi-Squared test, simple linear regression and multiple linear regression in terms that a 16-year-old would
understand
undefined:
In simple terms, the Chi-Squared test helps us see if there's a relationship between two categorical variables, like
checking if the ethnic ratios in a company differ by gender or if student proportions by year are different. Simple linear
regression is like drawing a line through points on a graph to see if one thing can predict another, such as predicting
yield in plants based on their height. Multiple linear regression is like simple linear regression but with more factors
involved, like predicting yield in plants based on height, weight, and fertilizer added. It helps us understand how
different factors together can influence an outcome, like predicting city population based on size, houses, apartments,
and jobs [38](#38), [42](#42), [46](#46).
20220691:
Explain the Correlation and difference with linear regression and multiple linear regression in terms that a 16-year-old
would understand
undefined:
Correlation shows how two things are related, like checking if height and weight in men have a connection. Linear
regression predicts one thing based on another, like predicting plant yield from height. Multiple linear regression is like
linear regression but with more factors, predicting plant yield from height, weight, and fertilizer added. Correlation looks
at the relationship between two things, while linear regression predicts one thing from another, and multiple linear
regression predicts using multiple factors together [44](#44).
20220691:
Explain non-parametric tests in terms that a 16-year-old would understand
undefined:
Non-parametric tests are like backup plans for when our data doesn't follow the usual rules. Imagine our data is like a
superhero movie, but sometimes it doesn't have the typical superhero shape. Non-parametric tests are like special tools
that can handle any shape our data takes, ensuring we can still analyze and understand it properly. They are like the
versatile sidekicks that come to the rescue when the main heroes (parametric tests) can't do the job because the data
doesn't fit their usual expectations [8](#8).
20220691:
Explain Kruskal Wallace test in terms that a 16-year-old would understand
undefined:
The Kruskal-Wallis test is like a detective looking for differences in groups when the data doesn't follow the usual rules.
Imagine comparing the speeds of different cars, but some cars are not following the speed limit rules. The Kruskal-Wallis
test helps us see if there are real differences in speeds among these cars, even when the data doesn't behave like we
expect. It's a special tool that works well when our data doesn't fit the standard assumptions, ensuring we can still find
important differences between groups effectively [56](#56).
20220691:
Explain Repeated Measures ANOVA and Multi-Way ANOVA and their differences in terms that a 16-year-old would
understand

10. undefined:
Repeated Measures ANOVA is like comparing how things change over time within the same group, such as tracking
blood serum levels at different months after treatment to see if there's a significant difference [60](#60). On the other
hand, Multi-Way ANOVA is like comparing different factors at once, like checking how treatment and age affect
outcomes together [66](#66). The main difference is that Repeated Measures ANOVA focuses on changes within one
group over time, while Multi-Way ANOVA looks at how multiple factors influence outcomes simultaneously. It's like
comparing how a plant grows over weeks (Repeated Measures) versus how sunlight, water, and soil affect growth
altogether (Multi-Way) in a garden experiment.
20220691:
Explain differences between multi-Way ANOVA involving 1 category of Interest and the difference with Multi-way
ANOVA involving > 1 categories of Interest in terms that a 16-year-old would understand
undefined:
In a multi-Way ANOVA with >1 category of interest, we look at more than one thing, like treatment, age, and cancer
stage, to see if they affect each other [66](#66). But in a multi-Way ANOVA with 1 category of interest, we focus on just
one thing, like treatment, while considering other factors like different hospital sections that need to be controlled for
[64](#64). So, it's like comparing how different medicines work across different ages and cancer stages versus just
looking at how medicines differ across different hospital sections.
20220691:
Explain Logistic regression and Poisson regression and their differences in terms that a 16-year-old would understand
undefined:
Logistic regression is like predicting if someone will pass or fail a test based on how much they studied. It's used when
we have two choices, like yes or no answers. Poisson regression is about predicting how often something happens, like
how many goals a soccer player scores in a game. It's used when we're looking at the rate of events over time. Logistic
regression is for yes/no questions, while Poisson regression is for counting how often something happens. [70](#70),
[72](#72).
------------------
20220691:
Summarize, simplify and explain the different test in this document
undefined:
A sample size is the number of animals or subjects needed for an experiment. It's important because a sample size that
is too small might not show the effect of interest, while a sample size that is too large can waste resources. The goal is to
have enough samples to detect an effect without using too many resources [4](#4).
20220691:
Explain the one mean amd two mean t test in terms that a 16-year-old would understand
undefined:
In a one mean t-test, we check if something is different from a specific value. For example, we might want to know if the
average income of college freshmen is less than $20,000. We collect data and compare it to this value to see if it's
significantly different. In a two mean t-test, we compare two groups to see if they are different from each other. For
instance, we could compare the average caloric intake of men and women to see if there's a significant difference
between them [14](#14) [18](#18).
20220691:
Explain the pairedt t test and the difference with other tests such as one mean and two mean t test in terms that a 16-
year-old would understand

11. undefined:
In a paired t-test, we compare measurements from the same group before and after something happens, like checking if
heart rate is higher after exercise compared to before. This is different from a one mean t-test where we compare one
group to a specific value, like checking if college freshmen earn less than $20,000. In a two mean t-test, we compare two
separate groups, like comparing the average body temperature of women to men to see if there's a significant
difference between them [20](#20) [15](#15) [16](#16).
20220691:
Explain the one-way ANOVA test and its importance in terms that a 16-year-old would understand
undefined:
The one-way ANOVA test checks if there is a difference in means among multiple groups, like comparing the weight lost
after different surgeries or white blood cell counts with different medications. It's like comparing the average scores of
students in different classes to see if one class performs significantly better. This test is important because it helps us
understand if there are real differences between groups, like if one surgery option leads to more weight loss than others
or if one medication regime affects white blood cell counts differently. It's a way to make fair comparisons and draw
meaningful conclusions from data [26](#26).
20220691:
Explain the single proportion test and the two proportions test and the difference with other tests in terms that a 16-
year-old would understand
undefined:
In a single proportion test, we check if one group's proportion is different from a specific value, like comparing the
cancer rate in North Dakota to the US average for males or females. This is different from a two proportions test where
we compare two groups to see if their proportions are different, such as comparing the passing rates of students taught
by different teachers. The single proportion test focuses on one group, while the two proportions test looks at
differences between two groups, like comparing boys and girls in a class to see if there's a significant difference in their
choices or outcomes [30](#30), [32](#32), [34](#34).
20220691:
Explain the Chi-Squared test, simple linear regression and multiple linear regression in terms that a 16-year-old would
understand
undefined:
In simple terms, the Chi-Squared test helps us see if there's a relationship between two categorical variables, like
checking if the ethnic ratios in a company differ by gender or if student proportions by year are different. Simple linear
regression is like drawing a line through points on a graph to see if one thing can predict another, such as predicting
yield in plants based on their height. Multiple linear regression is like simple linear regression but with more factors
involved, like predicting yield in plants based on height, weight, and fertilizer added. It helps us understand how
different factors together can influence an outcome, like predicting city population based on size, houses, apartments,
and jobs [38](#38), [42](#42), [46](#46).
20220691:
Explain the Correlation and difference with linear regression and multiple linear regression in terms that a 16-year-old
would understand
undefined:
Correlation shows how two things are related, like checking if height and weight in men have a connection. Linear
regression predicts one thing based on another, like predicting plant yield from height. Multiple linear regression is like
linear regression but with more factors, predicting plant yield from height, weight, and fertilizer added. Correlation looks

12. at the relationship between two things, while linear regression predicts one thing from another, and multiple linear
regression predicts using multiple factors together [44](#44).
20220691:
Explain non-parametric tests in terms that a 16-year-old would understand
undefined:
Non-parametric tests are like backup plans for when our data doesn't follow the usual rules. Imagine our data is like a
superhero movie, but sometimes it doesn't have the typical superhero shape. Non-parametric tests are like special tools
that can handle any shape our data takes, ensuring we can still analyze and understand it properly. They are like the
versatile sidekicks that come to the rescue when the main heroes (parametric tests) can't do the job because the data
doesn't fit their usual expectations [8](#8).
20220691:
Explain Kruskal Wallace test in terms that a 16-year-old would understand
undefined:
The Kruskal-Wallis test is like a detective looking for differences in groups when the data doesn't follow the usual rules.
Imagine comparing the speeds of different cars, but some cars are not following the speed limit rules. The Kruskal-Wallis
test helps us see if there are real differences in speeds among these cars, even when the data doesn't behave like we
expect. It's a special tool that works well when our data doesn't fit the standard assumptions, ensuring we can still find
important differences between groups effectively [56](#56).
20220691:
Explain Repeated Measures ANOVA and Multi-Way ANOVA and their differences in terms that a 16-year-old would
understand
undefined:
Repeated Measures ANOVA is like comparing how things change over time within the same group, such as tracking
blood serum levels at different months after treatment to see if there's a significant difference [60](#60). On the other
hand, Multi-Way ANOVA is like comparing different factors at once, like checking how treatment and age affect
outcomes together [66](#66). The main difference is that Repeated Measures ANOVA focuses on changes within one
group over time, while Multi-Way ANOVA looks at how multiple factors influence outcomes simultaneously. It's like
comparing how a plant grows over weeks (Repeated Measures) versus how sunlight, water, and soil affect growth
altogether (Multi-Way) in a garden experiment.
20220691:
Explain differences between multi-Way ANOVA involving 1 category of Interest and the difference with Multi-way
ANOVA involving > 1 categories of Interest in terms that a 16-year-old would understand
undefined:
In a multi-Way ANOVA with >1 category of interest, we look at more than one thing, like treatment, age, and cancer
stage, to see if they affect each other [66](#66). But in a multi-Way ANOVA with 1 category of interest, we focus on just
one thing, like treatment, while considering other factors like different hospital sections that need to be controlled for
[64](#64). So, it's like comparing how different medicines work across different ages and cancer stages versus just
looking at how medicines differ across different hospital sections.
20220691:
Explain Logistic regression and Poisson regression and their differences in terms that a 16-year-old would understand
undefined:
Logistic regression is like predicting if someone will pass or fail a test based on how much they studied. It's used when
we have two choices, like yes or no answers. Poisson regression is about predicting how often something happens, like

13. how many goals a soccer player scores in a game. It's used when we're looking at the rate of events over time. Logistic
regression is for yes/no questions, while Poisson regression is for counting how often something happens. [70](#70),
[72](#72).
20220691:
Explain Multilevel Modeling involving cluster randomized trials and Multilevel Model Multisite Randomized trials in
terms that a 16-year-old would understand
undefined:
Multilevel modeling involves looking at data that are grouped in a hierarchy, like clusters of hospitals or sites, where the
groups are not independent. In cluster randomized trials, entire clusters are randomly assigned to different treatments
to see if there are differences, like testing if a drug lowers blood pressure in patients. In multisite randomized trials, the
entire cluster is also randomly assigned, but the analysis is done in a two-level model to test treatment effects and
variances across sites. The sample size needed depends on factors like effect size, variance, and correlation within
clusters or sites. [78](#78) [76](#76)
20220691:
Explain Generalized Linear Mixed models in terms that a 16-year-old would understand
undefined:
Generalized Linear Mixed Models are like combining different types of models to analyze data. It's like using different
tools together to understand how things are related. These models are quite advanced and can handle a wide range of
situations by considering both fixed and random factors. To use them, you need to know how to set them up and define
what you're looking at. It's like having a special toolbox just for these models. You can explore more about them in R
using the Sample Size Calculation with GLMMs module. [83](#83).
------------------
20220691:
Explain Logistic regression and stepwise regression and their importance and differences in terms that a 16-year-old
would understand
undefined:
Logistic regression is like predicting if something will happen or not, such as having a symptom or not, by estimating the
chance of it occurring. It uses the logit of the proportion to make predictions, ensuring values make sense between 0
and 1. The method assumes linear relationships on a logistic scale, similar to regular regression models but for
dichotomous outcomes like 'yes or no' situations. The log odds ratios found in logistic regression help understand the
effects of predictor variables on the outcome, like how certain factors affect the likelihood of an event occurring. [1](#1).
Stepwise regression is a method to choose important predictor variables from a large set by adding or removing them
based on their impact on the model's performance. It's like a game of picking the best players for a team to win
matches. Stepwise regression can be forward (step-up) or backward (step-down), selecting variables that contribute the
most to explaining the outcome. It helps simplify complex models by selecting a small set of predictors for better
predictions. However, it can sometimes miss important variables if they are highly correlated with others. [3](#3).