### Initial comments

Traditionally when students first learn about the analysis
of experiments, there is a strong focus on hypothesis testing and making
decisions based on *p*-values. Hypothesis testing is important for
determining if there are statistically significant effects. However, readers
of this book should not place undo emphasis on *p*-values. Instead, they
should realize that *p*-values are affected by sample size, and that a low
*p*-value does not necessarily suggest a large effect or a practically meaningful
effect. Summary statistics, plots, effect size statistics, and practical
considerations should be used. The goal is to determine: a) statistical
significance, b) effect size, c) practical importance. These are all different
concepts, and they will be explored below.

### Statistical inference

Most of what we’ve covered in this book so far is about producing descriptive statistics: calculating means and medians, plotting data in various ways, and producing confidence intervals. The bulk of the rest of this book will cover statistical inference: using statistical tests to draw some conclusion about the data. We’ve already done this a little bit in earlier chapters by using confidence intervals to conclude if means are different or not among groups.

As Dr. Nic mentions in her article in the “References and further reading” section, this is the part where people sometimes get stumped. It is natural for most of us to use summary statistics or plots, but jumping to statistical inference needs a little change in perspective. The idea of using some statistical test to answer a question isn’t a difficult concept, but some of the following discussion gets a little theoretical. The video from the Statistics Learning Center in the “References and further reading” section does a good job of explaining the basis of statistical inference.

One important thing to gain from this chapter is an
understanding of how to use the *p*-value, *alpha*, and decision rule
to test the null hypothesis. But once you are comfortable with that, you will
want to return to this chapter to have a better understanding of the theory
behind this process.

Another important thing is to understand the limitations of
relying on *p*-values, and why it is important to assess the size of
effects and weigh practical considerations.

### Packages used in this chapter

The packages used in this chapter include:

• lsr

The following commands will install these packages if they are not already installed:

if(!require(lsr)){install.packages("lsr")}

### Hypothesis testing

#### The null and alternative hypotheses

The statistical tests in this book rely on testing a null hypothesis, which has a specific formulation for each test. The null hypothesis always describes the case where e.g. two groups are not different or there is no correlation between two variables, etc.

The alternative hypothesis is the contrary of the null hypothesis, and so describes the cases where there is a difference among groups or a correlation between two variables, etc.

Notice that the definitions of null hypothesis and alternative
hypothesis have nothing to do with what you want to find or don't want to find,
or what is interesting or not interesting, or what you expect to find or what
you don’t expect to find. If you were comparing the height of men and women,
the null hypothesis would be that the height of men and the height of women
were not different. Yet, you might find it surprising if you found this
hypothesis to be true for some population you were studying. Likewise, if you
were studying the income of men and women, the null hypothesis would be that
the income of men and women are not different, in the population you are studying.
In this case you might be *hoping* the null hypothesis is true, though you
might be *unsurprised* if the alternative hypothesis were true. In any
case, the null hypothesis will take the form that there is no difference
between groups, there is no correlation between two variables, or there is no
effect of this variable in our model.

#### p-value definition

Most of the tests in this book rely on using a statistic
called the *p*-value to evaluate if we should reject, or fail to reject,
the null hypothesis.

*Given the assumption that the null hypothesis is true*,
the *p*-value is defined as the probability of obtaining a result equal to
or more extreme than what was actually observed in the data.

We’ll unpack this definition in a little bit.

##### Decision rule

The *p*-value for the given data will be determined by
conducting the statistical test.

This *p*-value is then compared to a pre-determined
value *alpha*. Most commonly, an *alpha* value of 0.05 is used, but
there is nothing magic about this value.

If the *p*-value for the test is less than *alpha*,
we reject the null hypothesis.

If the *p*-value is greater than or equal to *alpha*,
we fail to reject the null hypothesis.

##### Coin flipping example

For an example of using the *p*-value for hypothesis
testing, imagine you have a coin you will toss 100 times. The null hypothesis
is that the coin is fair—that is, that it is equally likely that the coin will
land on heads as land on tails. The alternative hypothesis is that the coin is
not fair. Let’s say for this experiment you throw the coin 100 times and it
lands on heads 95 times out of those hundred. The *p*-value in this case
would be the probability of getting 95, 96, 97, 98, 99, or 100 heads, or 0, 1,
2, 3, 4, or 5 heads, *assuming that the null hypothesis is true*.

This is what we call a two-sided test, since we are testing both extremes suggested by our data: getting 95 or greater heads or getting 95 or greater tails. In most cases we will use two sided tests.

You can imagine that the *p*-value for this data will
be quite small. If the null hypothesis is true, and the coin is fair, there
would be a low probability of getting 95 or more heads or 95 or more tails.

Using a binomial test, the *p*-value is < 0.0001.

(Actually, R reports it as < 2.2e-16, which is shorthand
for the number in scientific notation, 2.2 x 10^{-16}, which is
0.00000000000000022, with 15 zeros after the decimal point.)

Assuming an *alpha* of 0.05, since the *p*-value
is less than *alpha*, we reject the null hypothesis. That is, we conclude
that the coin is not fair.

binom.test(5, 100, 0.5)

Exact binomial test

number of successes = 5, number of trials = 100, p-value < 2.2e-16

alternative hypothesis: true probability of success is not equal to 0.5

##### Passing and failing example

As another example, imagine we are considering two classrooms, and we have counts of students who passed a certain exam. We want to know if one classroom had statistically more passes or failures than the other.

In our example each classroom will have 10 students. The data is arranged into a contingency table.

__Classroom__ __Passed__ __Failed__

A 8 2

B 3 7

We will use Fisher’s exact test to test if there is an
association between *Classroom* and the counts of passed and failed
students. The null hypothesis is that there is no association between *Classroom*
and *Passed/Failed*, based on the relative counts in each cell of the
contingency table.

Input =("

Classroom Passed Failed

A 8 2

B 3 7

")

Matrix = as.matrix(read.table(textConnection(Input),

header=TRUE,

row.names=1))

Matrix

Passed Failed

A 8 2

B 3 7

fisher.test(Matrix)

Fisher's Exact Test for Count Data

p-value = 0.06978

The reported *p*-value is 0.070. If we use an *alpha*
of 0.05, then the *p*-value is greater than *alpha*, so we fail to
reject the null hypothesis. That is, we did not have sufficient evidence to
say that there is an association between *Classroom* and *Passed/Failed*.

More extreme data in this case would be if the counts in the upper left or lower right (or both!) were greater.

__Classroom__ __Passed__ __Failed__

A 9 1

B 3 7

__Classroom__ __Passed__ __Failed__

A 10 0

B 3 7

and so on, with Classroom B...

In most cases we would want to consider as
"extreme" not only the results when Classroom A has a high frequency
of passing students, but also results when Classroom B has a high frequency of
passing students. This is called a two-sided or two-tailed test. If we were
only concerned with one classroom having a high frequency of passing students,
relatively, we would instead perform a one-sided test. The default for the *fisher.test*
function is two-sided, and usually you will want to use two-sided tests.

__Classroom__ __Passed__ __Failed__

A 2 8

B 7 3

__Classroom__ __Passed__ __Failed__

A 1 9

B 7 3

__Classroom__ __Passed__ __Failed__

A 0 10

B 7 3

and so on, with Classroom B...

In both cases, "extreme" means there is a stronger
association between *Classroom* and *Passed/Failed*.

### Theory and practice of using *p*-values

#### Wait, does this make any sense?

Recall that the definition of the *p*-value is:

*Given the assumption that the null hypothesis is true*,
the *p*-value is defined as the probability of obtaining a result equal to
or more extreme than what was actually observed in the data.

The astute reader might be asking herself, “If I’m trying to determine if the null hypothesis is true or not, why would I start with the assumption that the null hypothesis is true? And why am I using a probability of getting certain data given that a hypothesis is true? Don’t I want to instead determine the probability of the hypothesis given my data?”

The answer is *yes*, we *would* like a method to
determine the likelihood of our hypothesis being true given our data, but we
use the *Null Hypothesis Significance Test* approach since it is
relatively straightforward, and has wide acceptance historically and across disciplines.

In practice we do use the results of the statistical tests to reach conclusions about the null hypothesis.

Technically, the *p*-value says nothing about the
alternative hypothesis. But logically, if the null hypothesis is rejected,
then its logical complement, the alternative hypothesis, is supported.
Practically, this is how we handle significant *p*-values, though this
practical approach generates disapproval in some theoretical circles.

#### Statistics is like a jury?

Note the language used when testing the null hypothesis.
Based on the results of our statistical tests, we either *reject *the null
hypothesis, or *fail to reject* the null hypothesis.

This is somewhat similar to the approach of a jury in a trial. The jury either finds sufficient evidence to declare someone guilty, or fails to find sufficient evidence to declare someone guilty.

Failing to convict someone isn’t necessarily the same as declaring someone innocent. Likewise, if we fail to reject the null hypothesis, we shouldn’t assume that the null hypothesis is true. It may be that we didn’t have sufficient samples to get a result that would have allowed us to reject the null hypothesis, or maybe there are some other factors affecting the results that we didn’t account for. This is similar to an “innocent until proven guilty” stance.

#### Errors in inference

For the most part, the statistical tests we use are based on
probability, and our data could always be the result of chance. Considering
the coin flipping example above, if we did flip a coin 100 times and came up
with 95 heads, we would be compelled to conclude that the coin was not fair.
But 95 heads *could* happen with a fair coin strictly by chance.

We can, therefore, make two kinds of errors in testing the null hypothesis:

• A *Type I error* occurs when the null hypothesis really
is true, but based on our decision rule we reject the null hypothesis. In this
case, our result is a *false* *positive*; we think there is an effect
(unfair coin, association between variables, difference among groups) when
really there isn’t. The probability of making this kind error is *alpha*,
the same *alpha* we used in our decision rule.

• A *Type II error* occurs when the null hypothesis is
really false, but based on our decision rule we fail to reject the null
hypothesis. In this case, our result is a *false negative*; we have
failed to find an effect that really does exist. The probability of making
this kind of error is called *beta*.

The following table summarizes these errors.

Reality

___________________________________

__Decision of Test__ __Null is true__ __Null is
false__

Reject null hypothesis Type I error Correctly

(prob. = alpha) reject null

(prob. = 1 – beta)

Retain null hypothesis Correctly Type II error

retain null (prob. = beta)

(prob. = 1 –
alpha)

##### Statistical power

The statistical power of a test is a measure of the ability
of the test to detect a real effect. It is related to the effect size, the
sample size, and our chosen *alpha* level.

The effect size is a measure of how unfair a coin is, how
strong the association is between two variables, or how large the difference is among
groups. As the effect size increases or as the number of observations we
collect increases, or as the *alpha* level increases, the power of the
test increases.

Statistical power in the table above is indicated by *1 –
beta*, and power is the probability of correctly rejecting the null
hypothesis.

An example should make these relationship clear. Imagine we
are sampling a large group of 7^{th} grade students for their height. That
is, the group is the population, and we are sampling a sub-set of these
students. In reality, for students in the population, the girls are taller
than the boys, but the difference is small (that is, the effect size is small),
and there is a lot of variability in students’ heights. You can imagine that
in order to detect the difference between girls and boys that we would have to
measure many students. If we fail to sample enough students, we might make a
Type II error. That is, we might fail to detect the actual difference in
heights between sexes.

If we had a different experiment with a larger effect size—for example the weight difference between mature hamsters and mature hedgehogs—we might need fewer samples to detect the difference.

Note also, that our chosen *alpha* plays a role in the
power of our test, too. All things being equal, across many tests, if we decrease
our *alph*a, that is, insist on a lower rate of Type I errors, we are more
likely to commit a Type II error, and so have a lower power. This is analogous
to a case of a meticulous jury that has a very high standard of proof to
convict someone. In this case, the likelihood of a false conviction is low,
but the likelihood of a letting a guilty person go free is relatively high.

#### The 0.05 alpha value is not dogma

The level of *alpha* is traditionally set at 0.05 in
some disciplines, though there is sometimes reason to choose a different value.

One situation in which the *alpha* level is increased
is in preliminary studies in which it is better to include potentially
significant effects even if there is not strong evidence for keeping them. In
this case, the researcher is accepting an inflated chance of Type I errors in
order to decrease the chance of Type II errors.

Imagine an experiment in which you wanted to see if various
environmental treatments would improve student learning. In a preliminary
study, you might have many treatments, with few observations each, and you want
to retain any potentially successful treatments for future study. For example,
you might try playing classical music, improved lighting, complimenting
students, and so on, and see if there is any effect on student learning. You
might relax your *alpha* value to 0.10 or 0.15 in the preliminary study to
see what treatments to include in future studies.

On the other hand, in situations where a Type I, false
positive, error might be costly in terms of money or people’s health, a lower *alpha*
can be used, perhaps, 0.01 or 0.001. You can imagine a case in which there is
an established treatment for cancer, and a new treatment is being tested.
Because the new treatment is likely to be expensive and to hold people’s lives
in the balance, a researcher would want to be very sure that the new treatment
is more effective than the established treatment. In reality, the researchers
would not just lower the *alpha* level, but also look at the effect size, submit
the research for peer review, replicate the study, be sure there were no
problems with the design of the study or the data collection, and weigh the
practical implications.

##### The 0.05 alpha value is almost dogma

In theory, as a researcher, you would determine the *alpha*
level you feel is appropriate. That is, the probability of making a Type I
error when the null hypothesis is in fact true.

In reality, though, 0.05 is almost always used in most
fields for readers of this book. Choosing a different *alpha* value will
rarely go without question. It is best to keep with the 0.05 level unless you
have good justification for another value, or are in a discipline where other
values are routinely used.

##### Practical advice

One good practice is to report actual *p*-values from
analyses. It is fine to also simply say, e.g. “The dependent variable was
significantly correlated with variable *A* (*p *< 0.05).” But I
prefer when possible to say, “The dependent variable was significantly
correlated with variable *A* (*p* = 0.026).

It is probably best to avoid using terms like “marginally
significant” or “borderline significant” for *p*-values less than 0.10 but
greater than 0.05, though you might encounter similar phrases. It is better to
simply report the *p*-values of tests or effects in straight-forward
manner. If you had cause to include certain model effects or results from
other tests, they can be reported as e.g., “Variables correlated with the dependent
variable with *p* < 0.15 were *A*, *B*, and *C*.”

#### Is the p-value every really true?

Considering some of the examples presented, it may have occurred
to the reader to ask if the null hypothesis is ever really true. For example,
in some population of 7^{th} graders, if we could measure everyone in
the population to a high degree of precision, then there must be *some*
difference in height between girls and boys. This is an important limitation
of null hypothesis significance testing. Often, if we have many observations,
even small effects will be reported as significant. This is one reason why it
is important to not rely too heavily on *p*-values, but to also look at
the size of the effect and practical considerations. In this example, if we sampled
many students and the difference in heights was 0.5 cm, even if significant, we
might decide that this effect is too small to be of practical importance,
especially relative to an average height of 150 cm. (Here, the difference
would be 0.3% of the average height).

### Effect sizes and practical importance

#### Practical importance and statistical significance

It is important to remember to not let *p*-values be
the only guide for drawing conclusions. It is equally important to look at the
size of the effects you are measuring, as well as take into account other practical
considerations like the costs of choosing a certain path of action.

For example, imagine we want to compare the SAT scores of
two SAT preparation classes with a *t*-test.

Class.A = c(1500, 1505, 1505, 1510, 1510, 1510, 1515, 1515, 1520,
1520)

Class.B = c(1510, 1515, 1515, 1520, 1520, 1520, 1525, 1525, 1530, 1530)

t.test(Class.A, Class.B)

Welch Two Sample t-test

t = -3.3968, df = 18, p-value = 0.003214

mean of x mean of y

1511 1521

The *p*-value is reported as 0.003, so we would
consider there to be a significant difference between the two classes (*p*
< 0.05).

But we have to ask ourselves the practical question, is a difference of 10 points on the SAT large enough for us to care about? What if enrolling in one class costs significantly more than the other class? Is it worth the extra money for a difference of 10 points on average?

#### Sizes of effects

It should be remembered that *p*-values do not indicate
the size of the effect being studied. It shouldn’t be assumed that a small *p*-value
indicates a large difference between groups, or vice-versa.

For example, in the SAT example above, the *p*-value is
fairly small, but the size of the effect (difference between classes) in this
case is relatively small (10 points, especially small relative to the range of
scores students receive on the SAT).

In converse, there could be a relatively large size of the
effects, but if there is a lot of variability in the data or the sample size is
not large enough, the *p*-value could be relatively large.

In this example, the SAT scores differ by 100 points between
classes, but because the variability is greater than in the previous example,
the *p*-value is not significant.

Class.C = c(1000, 1100, 1200, 1250, 1300, 1300, 1400, 1400, 1450,
1500)

Class.D = c(1100, 1200, 1300, 1350, 1400, 1400, 1500, 1500, 1550, 1600)

t.test(Class.C, Class.D)

Welch Two Sample t-test

t = -1.4174, df = 18, p-value = 0.1735

mean of x mean of y

1290 1390

boxplot(cbind(Class.C, Class.D))

#### p-values and sample sizes

It should also be remembered that *p*-values are affected
by sample size. For a given effect size and variability in the data, as the
sample size increases, the *p*-value is likely to decrease. For large
data sets, small effects can result in significant *p*-values.

As an example, let’s take the data from *Class.C* and *Class.D*
and double the number of observations for each without changing the distribution
of the values in each, and rename them *Class.E* and *Class.F*.

Class.E = c(1000, 1100, 1200, 1250, 1300, 1300, 1400, 1400, 1450,
1500,

1000, 1100, 1200, 1250, 1300, 1300, 1400, 1400, 1450, 1500)

Class.F = c(1100, 1200, 1300, 1350, 1400, 1400, 1500, 1500, 1550, 1600,

1100, 1200, 1300, 1350, 1400, 1400, 1500, 1500, 1550, 1600)

t.test(Class.E, Class.F)

Welch Two Sample t-test

t = -2.0594, df = 38, p-value = 0.04636

mean of x mean of y

1290 1390

boxplot(cbind(Class.E, Class.F))

Notice that the *p*-value is lower for the *t*-test
for *Class.E* and *Class.F* than it was for *Class.C* and *Class.D*.
Also notice that the means reported in the output are the same, and the box
plots would look the same.

#### Effect size statistics

One way to account for the effect of sample size on our statistical tests is to consider effect size statistics. These statistics reflect the size of the effect in a standardized way, and are unaffected by sample size.

An appropriate effect size statistic for a *t*-test is
Cohen’s *d*. It takes the difference in means between the two groups and
divides by the pooled standard deviation of the groups. Cohen’s *d*
equals zero if the means are the same, and increases to infinity as the
difference in means increases relative to the standard deviation.

In the following, note that Cohen’s *d* is not affected
by the sample size difference in the *Class.C* / *Class.D* and the *Class.E*
/ *Class.F* examples.

library(lsr)

cohensD(Class.C, Class.D,

method = "raw")

[1] 0.668

cohensD(Class.E, Class.F,

method = "raw")

[1] 0.668

Effect size statistics are standardized so that they are not
affected by the units of measurements of the data. This makes them interpretable
across different situations, or if the reader is not familiar with the units of
measurement in the original data. A Cohen’s *d* of 1 suggests that the
two means differ by one pooled standard deviation. A Cohen’s *d* of 0.5
suggests that the two means differ by one-half the pooled standard deviation.

For example, if we create new variables—*Class.G* and *Class.H*—that
are the SAT scores from the previous example expressed as a proportion of a
1600 score, Cohen’s *d* will be the same as in the previous example.

Class.G = Class.E / 1600

Class.H = Class.F / 1600

Class.G

Class.H

cohensD(Class.G, Class.H,

method="raw")

[1] 0.668

### Good practices for statistical analyses

#### Statistics is not like a trial

When analyzing data, the analyst should not approach the task as would a lawyer for the prosecution. That is, the analyst should not be searching for significant effects and tests, but should instead be like an independent investigator using lines of evidence to find out what is most likely to true given the data, graphical analysis, and statistical analysis available.

The problem of multiple p-values

One concept that will be in important in the following
discussion is that when there are multiple tests producing multiple *p*-values,
that there is an inflation of the Type I error rate. That is, there is a
higher chance of making false-positive errors.

This simply follows mathematically from the definition of *alpha*.
If we allow a probability of 0.05, or 5% chance, of making a Type I error for
any one test, as we do more and more tests, the chances that at least one of
them having a false positive becomes greater and greater.

*p*-value adjustment

One way we deal with the problem of multiple *p*-values
in statistical analyses is to adjust *p*-values when we do a series of
tests together (for example, if we are comparing the means of multiple groups).

###### Don’t use Bonferroni adjustments

There are various *p*-value adjustments available in
R. In some cases, we will use FDR, which stands for *false discovery rate*,
and in R is an alias for the Benjamini and Hochberg method. There are also
cases in which we’ll use Tukey range adjustment to correct for the family-wise
error rate.

Unfortunately, students in analysis of experiments courses
often learn to use Bonferroni adjustment for *p*-values. This method is
simple to do with hand calculations, but is excessively conservative in most
situations, and, in my opinion, antiquated.

There are other *p*-value adjustment methods, and the
choice of which one to use is dictated either by which are common in your field
of study, or by doing enough reading to understand which are statistically most
appropriate for your application.

#### Preplanned tests

The statistical tests covered in this book assume that tests
are preplanned for their *p*-values to be accurate. That is, in theory,
you set out an experiment, collect the data as planned, and then say “I’m going
to analyze it with kind of model and do these post-hoc tests afterwards”, and report
these results, and that’s all you would do.

Some authors emphasize this idea of preplanned tests. In contrast is an exploratory data analysis approach that relies upon examining the data with plots and using simple tests like correlation tests to suggest what statistical analysis makes sense.

If an experiment is set out in a specific design, then usually it is appropriate to use the analysis suggested by this design.

#### p-value hacking

It is important when approaching data from an exploratory
approach, to avoid committing *p*-value hacking. Imagine the case in
which the researcher collects many different measurements across a range of
subjects. The researcher might be tempted to simply try different tests and
models to relate one variable to another, for all the variables. He might
continue to do this until he found a test with a significant *p*-value.

But this would be a form of *p*-value hacking.

Because an *alpha* value of 0.05 allows us to make a
false-positive error five percent of the time, finding one *p*-value below
0.05 after several successive tests may simply be due to chance.

Some forms of *p*-value hacking are more egregious.
For example, if one were to collect some data, run a test, and then continue to
collect data and run tests iteratively until a significant *p*-value is
found.

##### Publication bias

A related issue in science is that there is a bias to
publish, or to report, only significant results. This can also lead to an
inflation of the false-positive rate. As a hypothetical example, imagine if
there are currently 20 similar studies being conducted testing a similar effect—let’s
say the effect of glucosamine supplements on joint pain. If 19 of those studies
found no effect and so were discarded, but one study found an effect using an *alpha*
of 0.05, and was published, is this really any support that glucosamine supplements
decrease joint pain?

### Clarification of terms and reporting on assignments

#### "Statistically significant"

In the context of this book, the term "significant" means "statistically significant".

Whenever the decision rule finds that *p* < *alpha*,
the difference in groups, the association, or the correlation under
consideration is then considered "statistically significant" or
"significant".

No effect size or practical considerations enter into
determining whether an effect is “significant” or not. The only exception is
that test assumptions and requirements for appropriate data must also be met in
order for the *p*-value to be valid.

What you need to consider:

• The null hypothesis

• *p*, *alpha*, and the decision rule,

• Your result. That is, whether the difference in groups, the association, or the correlation is significant or not.

##### What you should report on your assignments:

• The *p*-value

• The conclusion, e.g. "There was a significant difference in the mean heights of boys and girls in the class." It is best to preface this with the "reject" or "fail to reject" language concerning your decision about the null hypothesis.

#### “Size of the effect” / “effect size”

In the context of this book, I use the term "size of the effect" to suggest the use of summary statistics to indicate how large an effect is. This may be, for example the difference in two medians. I try reserve the term “effect size” to refer to the use of effect size statistics. This distinction isn’t necessarily common.

Usually you will consider an effect in relation to the magnitude of measurements. That is, you might look at the difference in medians as a percent of the median of one group or of the global median. Or, you might look at the difference in medians in relation to the range of answers. For example, a one-point difference on a 5-point Likert item. Counts might be expressed as proportions of totals or subsets.

What you should report on assignments:

• The size of the effect. That is, the difference in medians or means, the difference in counts, or the proportions of counts among groups.

• Where appropriate, the size of the effect expressed as a percentage or proportion.

• If there is an effect size statistic—such as *r*, *epsilon*-squared,
*phi*, Cramér's *V*, or Cohen's *d*—: report this and its
interpretation (small, medium, large), and incorporate this into your
conclusion.

#### "Practical" / "Practical importance"

If there is a significant result, the question of practical importance asks if the difference or association is large enough to matter in the real world.

If there is no significant result, the question of practical importance asks if the a difference or association is large enough to warrant another look, for example by running another test with a larger sample size or that controls variability in observations better.

What you should report on assignments:

• Your conclusion as to whether this effect is large enough to be important in the real world.

• The context, explanation, or support to justify your conclusion.

• In some cases you might include considerations that aren't included in the data presented. Examples might include the cost of one treatment over another, including time investment, or whether there is a large risk in selecting one treatment over another (e.g., if people's lives are on the line).

### A few of xkcd comics

#### Significant

#### Null hypothesis

#### P-values

### Experiments, sampling, and causation

#### Types of experimental designs

##### Experimental designs

A true experimental design assigns treatments in a systematic manner. The experimenter must be able to manipulate the experimental treatments and assign them to subjects. Since treatments are randomly assigned to subjects, a causal inference can be made for significant results. That is, we can say that the variation in the dependent variable is caused by the variation in the independent variable.

For interval/ratio data, traditional experimental designs can be analyzed with specific parametric models, assuming other model assumptions are met. These traditional experimental designs include:

• Completely random design

• Randomized complete block design

• Factorial

• Split-plot

• Latin square

#### Quasi-experiment designs

Often a researcher cannot assign treatments to individual experimental units, but can assign treatments to groups. For example, if students are in a specific grade or class, it would not be practical to randomly assign students to grades or classes. But different classes could receive different treatments (such as different curricula). Causality can be inferred cautiously if treatments are randomly assigned and there is some understanding of the factors that affect the outcome.

#### Observational studies

In observational studies, the independent variables are not manipulated, and no treatments are assigned. Surveys are often like this, as are studies of natural systems without experimental manipulation. Statistical analysis can reveal the relationships among variables, but causality cannot be inferred. This is because there may be other unstudied variables that affect the measured variables in the study.

#### Sampling

Good sampling practices are critical for producing good data. In general, samples need to be collected in a random fashion so that bias is avoided.

In survey data, bias is often introduced by a self-selection bias. For example, internet or telephone surveys include only those who respond to these requests. Might there be some relevant difference in the variables of interest between those who respond to such requests and the general population being surveyed? Or bias could be introduced by the researcher selecting some subset of potential subjects, for example only surveying a 4-H program with particularly cooperative students and ignoring other clubs. This is sometimes called “convenience sampling”.

In election forecasting, good pollsters need to account for selection bias and other biases in the survey process. For example, if a survey is done by landline telephone, those being surveyed are more likely to be older than the general population of voters, and so likely to have a bias in their voting patterns.

#### Plan ahead and be consistent

It is sometimes necessary to change experimental conditions during the course of an experiment. Equipment might fail, or unusual weather may prevent making meaningful measurements.

But in general, it is much better to plan ahead and be consistent with measurements.

##### Consistency

People sometimes have the tendency to change measurement frequency or experimental treatments during the course of a study. This inevitably causes headaches in trying to analyze data, and makes writing up the results messy. Try to avoid this.

##### Controls and checks

If you are testing an experimental treatment, include a *check*
treatment that almost certainly will have an effect and a *control*
treatment that almost certainly won’t. A *control* treatment will receive
no treatment and a *check* treatment will receive a treatment known to be
successful. In an educational setting, perhaps a control group receives no
instruction on the topic but on another topic, and the check group will receive
standard instruction.

Including checks and controls helps with the analysis in a practical sense, since they serve as standard treatments against which to compare the experimental treatments. In the case where the experimental treatments have similar effects, controls and checks allow you say, for example, “Means for the all experimental treatments were similar, but were higher than the mean for control, and lower than the mean for check treatment.”

##### Include alternate measurements

It often happens that measuring equipment fails or that a certain measurement doesn’t produce the expected results. It is therefore helpful to include measurements of several variables that can capture the potential effects. Perhaps test scores of students won’t show an effect, but a self-assessment question on how much students learned will.

##### Include covariates

Including additional independent variables that might affect the dependent variable is often helpful in an analysis. In an educational setting, you might assess student age, grade, school, town, background level in the subject, or how well they are feeling that day.

The effects of covariates on the dependent variable may be of interest in itself. But also, including co-variates in an analysis can better model the data, sometimes making treatment effects more clear or making a model better meet model assumptions.

### Optional discussion: Alternative methods to the Null Hypothesis Significance Test

#### The NHST controversy

Particularly in the fields of psychology and education, there has been much criticism of the null hypothesis significance test approach. From my reading, the main complaints against NHST tend to be:

• Students and researchers don’t really understand the meaning
of *p*-values.

• *p*-values don’t include important information like
confidence intervals or parameter estimates.

• *p*-values have properties that may be misleading, for
example that they do not represent effect size, and that they change with
sample size.

• We often treat an *alpha *of 0.05 as a magical cutoff
value.

Personally, I don’t find these to be very convincing arguments against the NHST approach.

The first complaint is in some sense pedantic: Like so many
things, students and researchers learn the definition of *p*-values at
some point and then eventually forget. This doesn’t seem to impact the usefulness
of the approach.

The second point has weight only if researchers use *only*
*p*-values to draw conclusions from statistical tests. As this book
points out, one should always consider the size of the effects and practical
considerations of the effects, as well present finding in table or graphical
form, including confidence intervals or measures of dispersion. There is no
reason why parameter estimates, goodness-of-fit statistics, and confidence
intervals can’t be included when a NHST approach is followed.

The properties in the third point also don’t count much as
criticism if one is using *p*-values correctly. One should understand
that it is possible to have a small effect size and a small *p*-value, and
vice-versa. This is not a problem, because *p*-values and effect sizes
are two different concepts. We shouldn’t expect them to be the same. The fact
that *p*-values change with sample size is also in no way problematic to
me. It makes sense that when there is a small effect size or a lot of variability
in the data that we need many samples to conclude the effect is likely to be
real.

(One case where I think the considerations in the preceding point are commonly problematic is when people use statistical tests to check for the normality or homogeneity of data or model residuals. As sample size increases, these tests are better able to detect small deviations from normality or homoscedasticity. Too many people use them and think their model is inappropriate because the test can detect a small effect size, that is, a small deviation from normality or homoscedasticity).

The fourth point is a good one. It doesn’t make much sense
to come to one conclusion if our *p*-value is 0.049 and the opposite
conclusion if our *p*-value is 0.051. But I think this can be ameliorated
by reporting the actual *p*-values from analyses, and relying less on *p*-values
to evaluate results.

Overall it seems to me that these complaints condemn poor
practices that the authors observe: not reporting the size of effects in some
manner; not including confidence intervals or measures of dispersion; basing
conclusions solely on *p*-values; and not including important results like
parameter estimates and goodness-of-fit statistics.

#### Alternatives to the NHST approach

##### Estimates and confidence intervals

One approach to determining statistical significance is to use estimates and confidence intervals. Estimates could be statistics like means, medians, proportions, or other calculated statistics. This approach can be very straightforward, easy for readers to understand, and easy to present clearly.

##### Bayesian approach

The most popular competitor to the NHST approach is Bayesian
inference. Bayesian inference has the advantage of calculating the probability
of the hypothesis *given the data*, which is what we thought we should be
doing in the “Wait, does this make any sense?” section above. Essentially it
takes *prior* knowledge about the distribution of the parameters of
interest for a population and adds the information from the measured data to
reassess some hypothesis related to the parameters of interest. If the reader
will excuse the vagueness of this description, it makes intuitive sense. We
start with what we suspect to be the case, and then use new data to assess our
hypothesis.

One disadvantage of the Bayesian approach is that it is not obvious in most cases what could be used for legitimate prior information. A second disadvantage is that conducting Bayesian analysis is not as straightforward as the tests presented in this book.

### References and further reading

** [Video] “Understanding statistical inference”**
from Statistics Learning Center (Dr. Nic). 2015. www.youtube.com/watch?v=tFRXsngz4UQ.

** [Video] “Hypothesis tests, p-value”** from
Statistics Learning Center (Dr. Nic). 2011. www.youtube.com/watch?v=0zZYBALbZgg.

*[Video]*** “Understanding the p-value”**
from Statistics Learning Center (Dr. Nic). 2011.

www.youtube.com/watch?v=eyknGvncKLw.

** [Video] “Important statistical concepts:
significance, strength, association, causation”** from

**Statistics Learning Center (Dr. Nic). 2012. www.youtube.com/watch?v=FG7xnWmZlPE.**

** “Understanding
statistical inference”** from Dr. Nic. 2015. Learn and Teach Statistics
& Operations Research. creativemaths.net/blog/understanding-statistical-inference/.

** “Basic concepts of hypothesis testing”** in
McDonald, J.H. 2014.

*Handbook of Biological Statistics*. www.biostathandbook.com/hypothesistesting.html.

** “Hypothesis testing”**, section 4.3, in Diez,
D.M., C.D. Barr , and M. Çetinkaya-Rundel. 2012.

*OpenIntro Statistics*, 2nd ed. www.openintro.org/.

*“Hypothesis Testing with One Sample”,*** sections
9.1–9.2 **in Openstax. 2013.

*Introductory Statistics*. openstax.org/textbooks/introductory-statistics.

** "Proving causation"** from Dr. Nic.
2013. Learn and Teach Statistics & Operations Research. creativemaths.net/blog/proving-causation/.

*[Video]*** “Variation and Sampling Error”**
from Statistics Learning Center (Dr. Nic). 2014. www.youtube.com/watch?v=y3A0lUkpAko.

*[Video]*** “Sampling: Simple Random,
Convenience, systematic, cluster, stratified”** from

**Statistics Learning Center (Dr. Nic). 2012. www.youtube.com/watch?v=be9e-Q-jC-0.**

** “Confounding variables”** in McDonald, J.H.
2014.

*Handbook of Biological Statistics*. www.biostathandbook.com/confounding.html.

** “Overview of data collection principles”**,
section 1.3, in Diez, D.M., C.D. Barr , and M. Çetinkaya-Rundel. 2012.

*OpenIntro Statistics*, 2nd ed. www.openintro.org/.

** “Observational studies and sampling strategies”**,
section 1.4, in Diez, D.M., C.D. Barr , and M. Çetinkaya-Rundel. 2012.

*OpenIntro Statistics*, 2nd ed. www.openintro.org/.

** “Experiments”**, section 1.5, in Diez, D.M.,
C.D. Barr , and M. Çetinkaya-Rundel. 2012.

*OpenIntro Statistics*, 2nd ed. www.openintro.org/.

* *

### Exercises F

1. Which of the following pair is the null hypothesis?

A) The number of heads from the coin is not different from the
number of tails.

B) The number of heads from the coin is different from the number of tails.

2. Which of the following pair is the null hypothesis?

A) The height of boys is different than the height of girls.

B) The height of boys is not different than the height of
girls.

3. Which of the following pair is the null hypothesis?

A) There is an association between classroom and sex. That is,
there is a difference in counts of girls and boys between the classes.

B) There is no association between classroom and sex. That is, there is no difference in counts of girls and boys between the classes.

4. We flip a coin 10 times and it lands on heads 7 times. We want to know if the coin is fair.

a. What is the null hypothesis?

b. Looking at the code below, and assuming an *alpha* of
0.05,

What do you decide (use the *reject* or *fail to reject*
language)?

c. In practical terms, what do you conclude?

binom.test(7, 10, 0.5)

Exact binomial test

number of successes = 7, number of trials = 10, p-value = 0.3438

5. We measure the height of 9 boys and 9 girls in a class, in centimeters. We want to know if one group is taller than the other.

a. What is the null hypothesis?

b. Looking at the code below, and assuming an *alpha*
of 0.05,

What do you decide (use the *reject* or *fail to
reject* language)?

c. In practical terms, what do you conclude? Address the practical importance of the results.

Girls = c(152, 150, 140, 160, 145, 155, 150, 152, 147)

Boys = c(144, 142, 132, 152, 137, 147, 142, 144, 139)

t.test(Girls, Boys)

Welch Two Sample t-test

t = 2.9382, df = 16, p-value = 0.009645

mean of x mean of y

150.1111 142.1111

mean(Boys)

sd(Boys)

quantile(Boys)

mean(Girls)

sd(Girls)

quantile(Girls)

boxplot(cbind(Girls, Boys))

6. We count the number of boys and girls in two classrooms. We are interested to know if there is an association between the classrooms and the number of girls and boys. That is, does the proportion of boys and girls differ statistically across the two classrooms?

a. What is the null hypothesis?

b. Looking at the code below, and assuming an *alpha*
of 0.05,

What do you decide (use the *reject* or *fail to
reject* language)?

c. In practical terms, what do you conclude?

Classroom__Girls__ __Boys__

A 13 7

B 5 15

Input =("

Classroom Girls Boys

A 13 7

B 5 15

")

Matrix = as.matrix(read.table(textConnection(Input),

header=TRUE,

row.names=1))

fisher.test(Matrix)

Fisher's Exact Test for Count Data

p-value = 0.02484

Matrix

rowSums(Matrix)

colSums(Matrix)

prop.table(Matrix,

margin=1)

### Proportions for each row

barplot(t(Matrix),

beside = TRUE,

legend = TRUE,

ylim = c(0, 25),

xlab = "Class",

ylab = "Count")

7. Why should you not rely solely on *p*-values to make
a decision in the real world? (You should have at least two reasons.)

8. Create your own example to show the importance of
considering the ** size of the effect**. Describe the scenario: what
the research question is, and what kind of data were collected. You may make
up data and provide real results, or report hypothetical results.

9. Create your own example to show the importance of
weighing ** other practical considerations**. Describe the scenario:
what the research question is, what kind of data were collected, what
statistical results were reached, and what other practical considerations were
brought to bear.

10. What is 5e-4 in common decimal notation?