# Directional claims require directional (statistical) hypotheses

*Author: Georgi Z. Georgiev, Published: Aug 6, 2018*

A sample of papers from any scientific journal, be it one on physics, economics, psychology, biology, medicine, etc. will inevitably reveal a most obvious truth: people care about the direction of the effect of whatever intervention or treatment one wants to study.

In most published papers and articles, we see **claims** of
"positive effect", "efficacy", "reduces negative effects", "results in
reduction of risk", "results in substantial increase in…", "decrease in…", "lowers
expenditure", "demonstrate improved…", "shows impaired…" yet we see next to many
such claims statements like "All statistical tests were two-sided." or p-values
result of calculations under a two-sided alternative (point null) and two-sided
confidence intervals.

This is quite peculiar since **a two-sided test is a
non-directional one and so are two-sided confidence intervals**. If we are
interested in making claims about the direction of the effect, a one-sided test
is clearly the test that corresponds to the questions we are asking, and it is
the test which we should adopt to support a directional claim. It is also the uniformly most-powerful test for the directional hypothesis and should be preferred for minimizing type II errors. The nominal type I error
associated with the claim is equal to the actual one, unlike when using a
two-tailed test, in which case the nominal uncertainty is larger than the
actual. For a set of examples of such misuses of two-tailed tests refer to "Examples of improper use of two-sided hypotheses".

This practice leads to **results that appear weaker than
they are** as well as to an increased number of false "negative" findings due
to the often-committed mistake of interpreting a high p-value as supporting the
null hypothesis without regard for statistical power. Why are then
practitioners reporting two-tailed probabilities and why are the statisticians who
consult them in favor of such practices?

I believe it is due to a misunderstanding about what a
one-sided p-value is combined with the stigma attached to using it from fellow
researchers, some journal guidelines and regulatory requirements as well as the
unfortunate design of many statistical tables, including the ones provided by
Fisher in his 1925 "Statistical Methods for Research Workers" ^{[1]}. You can see my
articles "Examples of negative portrayal of one-sided significance tests" and "Is
the widespread usage of two-sided tests a result of a usability/presentation issue" for more on the above.

Here I will examine why one-sided p-values and confidence intervals, and not two-sided ones, are the proper probability to support claims of directional effects.

## How to define a statistical null hypothesis

When we engage in reporting a p-value or a confidence
interval in a paper, we are performing statistical hypothesis testing, also
known as NHST or Null-Hypothesis Significance Testing. Putting the issues of
Fisherian versus N-P approaches aside I’ll assume the N-P approach in which we
have a null hypothesis and an alternative hypothesis, usually denoted H_{0}
and H_{1}.

The statistical null hypothesis should correspond to the default state of matters: in different contexts it may correspond to prior well-established knowledge or to a possible state of the universe we want to reject only if we observe results sufficiently improbable if it were in fact true. In a decision-making context that is usually what corresponds to "no worse than or equal to the currently preferred solution".

If one thinks in terms of arguing or convincing someone, then the null hypothesis will be the argument one will be faced with e.g. "your new molecule is garbage and is in fact worse than or no better than placebo" or "your new medicine is harming people rather than healing them".

In a Fisherian or N-P test we calculate the probability of observing results as extreme or more extreme than the observed, assuming the aforementioned null hypothesis is true – the p-value. In doing so we are addressing the concerns, objections and critiques of any adversary or opponent.

When we observe a statistically significant result and use
it as evidence, we are essentially saying: **granting your rival theory to be
true, we would only see such data in 1 out of N tests performed using this
statistical procedure**. Thus, we have either seen a very rare and thus
unlikely occurrence, or we would have to reject the null hypothesis (and accept
the complementary and necessarily composite alternative one). It is the
probabilistic equivalent of the logical procedure known as reductio ad absurdum:
assuming a premise and then rejecting it after reaching a contradiction*.

Naturally, the choice of N should be such that it appropriately reflects the range of risks and rewards related to making the decision to reject the null hypothesis. The more skewed the balance towards risks, the larger N should be. N should be agreed upon before the experiment that if it produced data of a given level of significance, then the null should be rejected. Our opponent cannot claim that no amount of data can reject their hypothesis as in doing so they would be requiring the impossible (0% uncertainty / 100% certainty). It would be equivalent to conceding that they have an irrational belief which cannot be falsified by any achievable quantity of data.

Note that in all the above the null hypothesis is just a claim. In an experimental scenario it is a research hypothesis to be put to the test, to try to falsify as Karl Popper would say.

So, how should one define the null hypothesis? In most
situations, we first define the alternative H_{1}, which is usually **the
outcome we hope to see**. This is the outcome under which we discover
something new and useful, make money, etc. Then the null H_{0} is what
is left of the sampling space. So, if we really want our new intervention,
treatment, device, etc. to be effective, the null hypothesis would be that it is,
at best, ineffective, or at worst: does damage, achieves the contrary of what
we wanted to achieve with it.

Another approach is to ask: **what would an adversary (detractor,
opponent or just a sceptic) like to be able say about my intervention,
treatment, device, etc.**? What would he want to prove as true? And then
define this as our null hypothesis (H_{0}), essentially consenting to
their claim in a hypothetical way. Then one can define the alternative H_{1}
as covering the rest of the possible outcomes.

I do not believe one of these approaches to be better or
worse than the other and logically they should result in the same mathematical
definitions of H_{0} and H_{1.}

What should be evident from the above is that if one is
precise in their definitions there should not be any possibility of **conflating
the null hypothesis with the nil hypothesis**. While the two might coincide
in some rare cases, equating them is logically invalid.

## What is a directional null or alternative hypothesis?

I should note that when speaking about a directional
hypothesis one usually refers to a directional alternative: H_{1} for
which the complimentary null is also directional but includes zero. For example,
H_{1}: µ_{1} > µ_{0} is a right-sided alternative
and its corresponding null is H_{0}: µ_{1} ≤ µ_{0}.
Some refer to such a null as a directional null and I think it is not wrong as
long as it is clear that it contains zero. Explicit statement is preferred,
though.

**Any claim in which a comparison is made between two values (including
an observed value and a baseline) that involves stating the exact value of the difference
is a directional claim.**

While it is often accompanied by qualifying statements such as "is greater than", "is more efficient than", "reduces", "lowers", etc. as already mentioned in the beginning of the article, its presence is not necessary for the claim to be qualified as directional. For example, stating "there is difference between the treatment and control group of 0.4" is already a directional statement since it is referring to γ, not |γ|. I do not think anyone thinks when seeing "0.4" that it could be "0.4 or -0.4" and this is only natural.

Under this definition which I believe to be precise, most, if not all claims made in published research are directional. If you have an example where the absolute value of the difference is of interest and not the observed value, please let me know as I’m blissfully unaware of such cases for now.

## Determining which p-value and confidence interval boundary to report

If we want to report a p-value or a confidence interval to
support a directional claim we need to consider what is an **appropriate null
hypothesis** under the assumption of which the probability for the interval or
p-value is to be calculated. The procedure would be to make the counterclaim and
to translate it into a numerical (statistical) form.

For example, if the claim is that a treatment is more
efficient than an existing one, without specifying by how much, then we can use
the simple hypotheses: H_{0}: µ_{1} ≤ µ_{0} and
H_{1}: µ_{1} > µ_{0}.

If, however, we want to claim some level of superiority, say
that the absolute difference in proportions is 0.2 or that it is 30% better or
that it reduces relative risk by 50%, then we should choose our hypothesis more
carefully as our critics will now have a different null hypothesis: H_{0}:
µ_{1} ≤ µ_{0} + ɛ where ɛ is the claimed level
of superiority.

In such a case, if the observed difference is, say, 0.2 and we want to claim a given level of superiority at a given level of certainty (say 95%), then we should take the 95% one-sided interval, for example spanning [0.05, +∞). In this case we can say that we can reject the null that the treatment is less than 0.05 more efficient than the existing one at the 95% significance level and accept the alternative that it is more than 0.05 more efficient. Multiple confidence intervals at different levels of significance can be calculated to support other claims that can be supported with greater or less certainty.

## Reporting p-values for directional claims

Given the above, I find the current way of reporting
p-values suboptimal and somewhat to blame for the myriad of p-value misinterpretations
that we see. Take for example: "the treatment was found to be effective in increasing
the survival rate by 0.2 (p=0.02)". **What is the p-value calculated for?**

Let us denote with δ = µ_{1} - µ_{0}. Is
the reported p-value computed under the null hypothesis of no effect H_{0}:
δ = 0 (1)? Is it for H_{0}: δ ≤ 0 (2)? Is it for H_{0}:
δ ≤ 0.2 (3)? Each of these nulls results in a different p-value!

Most of the time we would like to
know p under (2) and sometimes under (3) for H_{0}: δ ≤ L_{b}
where 0 ≤ L_{b} ≤ 0.3 and is some kind of uncertainty bound,
but instead we get it computed under (1). Given that, it is hardly surprising
that people are confused about how to interpret p-values. If they want (2) then
they are seeing a nominal probability which is two times higher than the
actual. If they want (3) then most of the time they are seeing a nominal
probability which is much lower than the actual.

Some authors have the good sense to report a confidence interval around the observed value. Unfortunately, most of the time it is also constructed under (1) so it is overestimating the error in judgement one would make if they compare values to either interval bound. In such cases the p-value is likely improperly calculated as well (usually under (1)).

Such ambiguities would be avoided to a significant extent if
the null hypothesis is explicitly stated next to the p-value. For example: "the
treatment was found to be effective in increasing the survival rate by 0.2
(p=0.01; H_{0}: µ_{1} - µ_{0} ≤ 0)" for a simple
one-sided hypothesis.

## What if we see an unexpected result (in the opposite direction)?

The incorrect matching between a research and a statistical hypothesis seems to be the reason why this question is asked at all. In some cases, the mismatch might be due to incorrect reasoning and in others it could be due to blind application of procedures. Or maybe it is due to the apparent "Paradox of one-sided vs. two-sided tests of significance"?

Whatever the reason, the idea is that according to some if we register a study with a type I error α for a one-sided test in one direction then we should not conduct a one-sided test in the other direction after seeing an unexpected result. Doing so is chastised really harshly and can result in name-calling. Understanding the need for correspondence between a statistical and a research hypothesis combined with the fact that our hypothesis has no impact on the sampling space can help us see through that non-issue.

In a well-thought-out experiment the statistical hypothesis will relate to the research hypothesis in a meaningful manner and support its claim, but they are rarely the same thing, as it would only happen when the research hypothesis is a basic statement about the true value of the parameter of interest.

For example, the research hypothesis might be "Intervention X has a positive effect on population Y (due to A, B, C)". The null hypothesis, or the thing we do not want to dispose of easily, is thus that "Intervention X relying on A, B, C has negative or no effect on population Y". The paper that would be written if the null hypothesis is rejected in the experiment would be titled "Intervention X beneficial for Y".

Translated to a well-defined statistical hypothesis it becomes "Intervention X has a positive effect on some measure Z in population Y". The null hypothesis, or the thing we do not want to dispose of easily is thus that "Intervention X has negative or no effect on measure Z in population Y". The paper that would be written if the null hypothesis rejected in the experiment would be titled "Intervention X beneficial for improving Z in Y". The maximum probability for a type I error would be controlled by conducting a one-sided test of the appropriate form.

However, if it turns out that intervention X demonstrates a negative effect of significant magnitude the most natural thing to do is to conduct a one-sided test of significance in the negative direction. The question is changed and so is the null hypothesis and the paper that would be published if the null is rejected will now be titled "Intervention X harmful for population Y based on measures of Z".

If the question was: "is there an effect of intervention X
in the positive or the negative direction" then a two tailed test would be
appropriate, however this non-directional question is rarely what interests us.
We usually care both about the presence of an effect and its direction, so we
care about a **narrower set of possible answers to a question broader** than
the one answered by a two-sided alternative hypothesis.

That such an unexpected results will likely disagree with a lot of published literature and be hard to explain for the researcher and the scientific community has no bearing on the validity of the statistical test that best corresponds to the research claim warranted by the data.

## Severe Testing and reporting SEV(claim)

The approach of Deborah Mayo & Aris Spanos ^{[2,3]} with the **severity criterion** is
trying to address the issue of matching claims and hypotheses in a structured and
philosophically defensible way by calculating the severity with which a claim
can be made given some data. Informally, a statistical hypothesis or claim can
be said accepted only to the extent to which it has passed a severe test, that
is, a procedure, which if it was false, would have detected that with high
probability.

Severity is a post-hoc test which, like power, is calculated
for a point alternative hypothesis and provides an assessment of the
uncertainty (error) related to inferences about it. Severity provides error
probabilities both when we reject H_{0} and when we want to accept it,
depending on whether a pre-specified level of significance has been attained.

* Note: D.Mayo & A.Spanos argue for treating it not as reaching a
contradiction but as "strong argument from coincidence" when referring to the
case at hand and not relying on explanations based on long-run errors or
behavior-guiding qualities of procedures (Mayo & Spanos, 2006, 2010) ^{[2,3]})

I thank Prof. Daniel Lakens for sanity-checking this article.

#### References

[1] Fisher R.A. (1925) "Statistical methods for research workers". Oliver & Boyd, Edinburg

[2] Mayo D.G., Spanos A. (2006) "Severe testing as a basic concept in a Neyman–Pearson philosophy of induction", *The British Journal for the Philosophy of Science*, Volume 57-2:323–357; https://doi.org/10.1093/bjps/axl003

[3] Mayo D.G., Spanos A. (2010) "Error statistics", in P. S. Bandyopadhyay & M. R. Forster (Eds.), Philosophy of Statistics, (7, 152–198). *Handbook of the Philosophy of Science*. The Netherlands: Elsevier; ISBN: 9780444518620

**Enjoyed this article? Please, consider sharing it where it will be appreciated!**

#### Cite this article:

If you'd like to cite this online article you can use the following citation:

Georgiev G.Z., *"Directional claims require directional (statistical) hypotheses"*, [online] Available at: https://www.onesided.org/articles/directional-claims-require-directional-hypotheses.php URL [Accessed Date: 15 Nov, 2019].