Null hypothesis: what is it and what is it used for in science?

What is the null hypothesis and what is it used for in the world of scientific research? Let's take a look at it.

In the world of science, statistics is the basis of any statement. After all, numbers do not lie, since they comprise a proper and objective reality applicable to all processes, regardless of the culture or geographical distance of the person who applies them.

Thus, in order to be able to affirm (or rather, to suspect) that we have discovered something, we must be able to present reliable and repeatable data in a numerical language that supports it. In the world of experimentation, there must exist an anchor point that we try to disprove from the beginning, i.e., the null hypothesis..

Statistics and the scientific method seem to be disciplines and methodologies that are too complex for the general population, but nothing could be further from the truth. In this opportunity, we open a small window into the world of numerical realities and basal science with an explanation of what the null hypothesis is.

What is the null hypothesis: refuting assumptions.

To be able to move comfortably in the world of the hypotheses, it is necessary that first we cement the essential bases for the understanding of the subject. Vet us dive, albeit briefly, into the world of the scientific method..

About the scientific method

The scientific method is defined as a research method based on empirics and measurement, which is also subject to the specific principles of reasoning tests. This concatenation of steps and reasoning is based on two main pillars:

• Reproducibility: the ability to repeat any experiment with the necessary means, if a person is willing to do so.
• Refutability: every scientific proposition must be susceptible to being falsified or refuted.

In the world of science we never move in absolute dogmas. No matter how much a number supports a hypothesis, it is possible that it does not fully represent reality, or that it has not been taken into account.For example, it is possible that extrinsic factors have not been taken into account in the experiment or that the sample size is not large enough.

Thus, the scientific method is based on observation, measurement, hypothesis, reproducibility, refutability, and review by agents external to those who have to those who have performed the experiment itself.

If any avid reader of scientific knowledge comes across a typical paper in any journal such as Science or Nature, he or she will notice that the researchers seem to be anything but sure of their findings. "Could be", "could mean", "this seems to indicate", "maybe it exists" and other phrases dominate the paragraphs.

Moreover, any self-respecting research skirts in its last lines that "further experimentation is required to delve deeper into the subject under discussion." As we have seen, science, despite what the general population believes, is based more on discarding falsehoods than on affirming absolute dogmas..

Now that we have understood the caution and mistrust that we must have in the world of science, it is time to explain what the null hypothesis is.

The false statement

According to the Real Academia Española de la lengua, a hypothesis is defined as a supposition of something possible or impossible in order to draw a consequence from it. If we go to its etymological roots, we will see that the meaning of the word is contained in the same, since "hypo" corresponds to "subordination / below" and "thesis" to "a conclusion that is maintained by reasoning".

The hypothesis is an unverified statement that requires a contrastation with experience (i.e. an experiment) and a (i.e., an experiment) and after being refuted and tested, in the best case, it can become a verified statement.

In any case, to affirm that something "is", we must also rule out that "is not", right? Don't despair, because we present this abstraction exercise in a kinder way in the following lines.

Let's take an example: we want to demonstrate that humidity plays an essential role in the spawning of a population of insects of a particular species in an ecosystem. In this case, we have two possible hypotheses:

• That humidity does not influence the number of eggs per spawning, so there will be no difference in the mean of this number according to climate and region. (H0)
• That humidity does influence the number of eggs per spawning. There will be significant differences in the mean depending on the specific parameter measuring humidity. (H1)

The null hypothesis (H0) in this case corresponds to the first of the statements. Thus, we can define the null hypothesis as a statement about a parameter that claims that two or more events are not correlated with each other..

This concept is the basis of scientific hypotheses, because no matter how much you want to demonstrate a relationship between two specific parameters, you have to operate on the fact that if it has not been documented, it is because it does not exist. Moreover, any reliable research must do everything possible to test its hypothesis H1 (that the suspected correlation does exist). It is not a matter of obtaining the desired result "with", but of arriving at it "in spite of"..

The importance of P-value

Attentive readers will have noticed that, in the above example of humidity, the hypothesis showing a correlation between this parameter and the mean number of eggs contains an important term in it: the significance of the P-value. an important term in it: the significance.

This is essential, because observing different means in the number of insect eggs, however real and observable, may be a non-significant event, i.e., the product of random sampling beyond correlation.

For example, if an alien came to earth and picked four 50-year-old men at random and three of them were 6 feet tall, he could safely say that 3 out of 4 humans are very tall. These data are not statistically significant, as they are due to the randomness of the sample. On the other hand, if such an alien were to measure 3 million citizens and record the variations in height in all geographic locations of the world, then perhaps he would observe significant differences in the height of the species according to (x) parameters.

All these conjectures are not based on a mere reasoning process, since there are numbers that reflect the significance of the data obtained. This is the case of the "P-value", a numerical figure that is defined as the probability that a calculated statistical value is possible given a certain null hypothesis.. This figure is a probability ranging from 0 to 1.

Thus, we are interested in the P-value being low, very low. In general, it can be said that an H0 hypothesis (recall, the null hypothesis) can be rejected when this number is equal to or less than an arbitrarily set significance level (generally 0.05). This means that the probabilities that the results obtained are the product of chance (i.e. that there is no null hypothesis) can be rejected. (i.e. that there is no correlation between the parameters, i.e. that the null hypothesis is true) are very, very low.

It should be emphasized that, in any case, hypothesis testing does not allow us to accept a hypothesis in its entirety, but to reject it or not. Again using the example of eggs and insects, if we obtain samples of 300 spawns from 300 different females in 30 different locations and there are significant differences in the means according to the humidity of the ecosystem, we can say that there seems to be a relationship between the size of the cohort and the humidity parameter.

What we cannot, in any case, is to affirm this as an immovable dogma. The scientific method is based on repetition and refutability, so different research teams will have to repeat the experiment. different research teams must repeat the experiment carried out under the same conditions and obtain equally significant results in order for the correlation to be reliable. so that the correlation can be reliable and valid.

Even so, no matter how cemented the idea is in the scientific community, an entomologist may come along and discover that, after dissecting 300 females of that species, it turns out that the red ones have a larger ovipositor apparatus and therefore lay a higher average number of eggs. Now what?

Conclusions

As we have wanted to convey in these lines, science and the scientific method in general are a series of exciting processes, but certainly frustrating, because we do not stop moving in assumptions that can be refuted at any time.

Faced with the question "what is the null hypothesis?" we can affirm that it is the basis of any investigation, since it corresponds to the supposed reality that we want to deny, that is, that there is no correlation between the parameters that we have proposed to investigate.

Bibliographical references:

• How is a statistical contrast approached? Null hypothesis vs. alternative hypothesis. Ub.edu.
• Anderson, D. R., Burnham, K. P., & Thompson, W. L. (2000). Null hypothesis testing: problems, prevalence, and an alternative. The journal of wildlife management, 912-923.
• Scientific method, Universidad Complutense de Madrid. Retrieved August 17 from https://www.ucm.es/data/cont/docs/107-2016-02-17-El%20M%C3%A9todo%20Cient%C3%ADfico.pdf.
• Suarez, N. R. (2012). The revolution in statistical decision making: the p-value. Telos, 14(3), 439-446.