Tag Archives: hypothesis

What makes a good hypothesis?

Credit: Pixabay.

A hypothesis is a precise and testable statement of what a researcher predicts will be the outcome of a study. This usually involves proposing a relationship between two or more variables.

Verifying a hypothesis, also sometimes referred to as a working statement, requires using the scientific method, usually by designing an experiment.

For instance, one common adage is ‘an apple a day keeps the doctor away’. If we use this aphorism as our hypothesis then we can make a prediction that consuming at least one apple per day should result in fewer visits to the doctor than the general population that eats apples sparingly or never.

In 2015, researchers at Dartmouth College, the University of Michigan School of Nursing, and the Veteran Affairs Medical Center in White River actually investigated this hypothesis. They combed national nutrition data collected from nearly 8,400 men and women — 753 of whom ate an apple a day. The study found that “evidence does not support that an apple a day keeps the doctor away; however, the small fraction of US adults who eat an apple a day do appear to use fewer prescription medications.”

So perhaps there’s a glimmer of truth to this hypothesis, but not necessarily because apples are some miracle foods. It could be that people who eat apples every day also consume other fresh produce and less processed foods than the general population, a diet that helps to prevent obesity, a huge risk factor for a myriad of illnesses such as hypertension and diabetes that require prescription medication. This is why hypotheses need to be defined as precisely and as narrowly as possible in order to isolate confounding effects.

Types of hypothesis

The ‘apple a day’ study is an example of an alternative hypothesis, which states that there is a relationship between two variables being studied, the daily apple consumption and visits to the GP. One variable, called the independent variable, has an effect on the other, known as the dependent variable. The independent variable is what you change and the dependent variable is what you measure. For example, if I am measuring how a plant grows with different fertilizers, the fertilizers are what I can change freely (independent) while the plant’s growth would be dependent on what it is given. In order for an alternative hypothesis to be validated, the results have to have statistical significance in order to rule out chance.

Examples of alternative hypotheses:

  • Dogs wag their tails when they’re happy.
  • The accumulation of greenhouse gases in the atmosphere raises global average temperature.
  • Wearing a seatbelt reduces traffic-related fatalities.
  • Students who attend class earn higher scores than students who skip class.
  • People exposed to higher levels of UV light have a higher incidence of skin cancer than the general population.

Another common type of hypothesis used in science is the null hypothesis, which states that there is no relationship between two variables. This means that controlling one variable has no effect on the other. Any results are due to chance and thus pursuing a cause-effect relationship between the two variables is futile.

The null hypothesis is the polar opposite of the alternative hypothesis since they contain opposing viewpoints. In fact, the latter is called this way because it is an alternative to the null hypothesis. An apple a day doesn’t keep the doctor away, you could propose if you were designing a null hypothesis experiment.

Examples of null hypotheses:

  • Taking an aspirin a day doesn’t reduce the risk of a heart attack.
  • Playing classical music doesn’t help plants grow more biomass.
  • Vaccines don’t cause autism.
  • Hyperactivity is unrelated to sugar consumption.

The acceptance of the alternative hypothesis, often denoted by H1, depends on the rejection of the null hypothesis (H0). A null hypothesis can never be proven, it can only be rejected. To test a null hypothesis and determine whether the observed data is not due to change or the manipulation of data, scientists employ a significance test.

Rejecting the null hypothesis does not necessarily imply that a study did not produce the required results. Instead, it sets the stage for further experimentation to see if a relationship between the two variables truly exists.

For instance, say a scientist proposes a null hypothesis stating that “the rate of plant growth is not affected by sunlight.” One way to investigate this conjecture would be to monitor a random sample of plants grown with or without sunlight. You then measure the average mass of each group of plants and if there’s a statistically significant difference in the observed change, then the null hypothesis is rejected. Consequently, the alternate hypothesis that “plant growth is affected by sunlight” is accepted, then scientists can perform further research into the effects of different wavelengths of light or intensities of light on plant growth.

At this point, you might be wondering why we need the null hypothesis. Why not propose and test an alternate hypothesis and see if it is true? One explanation is that science cannot provide absolute proofs, but rather approximations. The scientific method cannot explicitly “prove” propositions. We can never prove an alternative hypothesis with 100% confidence. What we can do instead is reject the null hypothesis, supporting the alternative hypothesis.

It just so happens that it is easier to disprove a hypothesis than to positively prove one. But the supposition that the null hypothesis is incorrect allows for a stable foundation on which scientists can build. You can view it this way: the results from testing the null hypothesis lay the groundwork for the alternate hypothesis, which explores multiple ideas that may or may not be correct.

The alternative and null hypotheses are the two main types you’ll encounter in studies. But the alternative hypothesis can be further broken down into two categories: directional and nondirectional alternative hypotheses.

The directional alternative hypothesis predicts that the independent variable will have an effect on the dependent variable and the direction in which the change will take place. The nondirectional alternative hypothesis predicts the independent variable will have an effect but its direction is not specific, without stating the magnitude of the difference.

For instance, a non-directional hypothesis could be “there will be a difference in how many words children and adults can recall,” while the directional hypothesis could predict that “adults will recall more words than children.”

Hypotheses can be simple or complex. A simple hypothesis predicts a relationship between a single dependent variable and a single independent variable while a complex one predicts a relationship between two or more independent and dependent variables. An example of a complex hypothesis could be “Do age and weight affect the chances of getting diabetes and heart diseases?” There are two independent and two dependent variables in this statement whose relationship we seek to verify.

How to write a good hypothesis

The way you formulate a hypothesis can make or break your research because the validity of an experiment and its results rely heavily on a robust testable hypothesis. A good research hypothesis typically involves more effort than a simple guess or assumption.

Generally, a good hypothesis:

  • is testable, meaning it must be possible to show that a hypothesis is true or false, and the results of this investigation have to be replicable;
  • includes both an independent and dependent variable.
  • allows for the manipulation of the variables ethically.
  • has clear and focused language. Don’t be vague.
  • is related to other published research.
  • is written, either explicitly or not, as an “if-then” statement because we can then make a prediction of the outcome of an experiment.

An example of a testable good hypothesis is a conjecture such as “Students recall more information during the afternoon than during the morning.” The independent variable is the time of the lecture and the dependent variable is the recall of the information presented in the lecture, which can be verified with standardized tests.

A bad hypothesis could be something like “Goldfish make better pets than cats.” Right off the bat, you can see a couple of problems with this statement. What constitutes a good pet? Is a good pet fluffy and interactive or one that is low maintenance? Can I predict whether a cat or goldfish will make for a good pet? This is more a matter of opinion that doesn’t provide any meaningful results.

Often, the best hypotheses start from observation. For instance, everybody has witnessed that objects that are thrown into the air will fall toward the ground. Sir Isaac Newton formulated a hypothesis in the 17th-century that explains this observation, stating that ‘objects with mass attract each other through a gravitational field.’

But despite Newton’s hypothesis being very well written, in the sense that it is testable, simple, clear, and universal, we now know it was wrong. In the 20th-century, Albert Einstein showed that a hypothesis that more precisely explains the observed phenomenon is that ‘objects with mass cause space to bend.’ The lesson here is that all hypotheses are temporary and partial, they’re never permanent and irrefutable. This is also a good example of why the null hypothesis is so paramount.

Hypothesis formulation and testing through statistical methods are integral parts of the scientific method, the systematic approach to assessing whether a statement is true or false. All the best stories in science start with a good hypothesis. 

Three Old Scientific Concepts Getting a Modern Look

If you have a good look at some of the underlying concepts of modern science, you might notice that some of our current notions are rooted in old scientific thinking, some of which originated in ancient times. Some of today’s scientists have even reconsidered or revamped old scientific concepts. We’ve explored some of them below.

4 Elements of the Ancient Greeks vs 4 Phases of Matter

The ancient Greek philosopher and scholar Empedocles (495-430 BC) came up with the cosmogenic belief that all matter was made up of four principal elements: earth, water, air, and fire. He further speculated that these various elements or substances were able to be separated or reconstituted. According to Empedocles, these actions were a result of two forces. These forces were love, which worked to combine, and hate, which brought about a breaking down of the elements.

What scientists refer to as elements today have few similarities with the elements examined by the Greeks thousands of years ago. However, Empedocles’ proposed quadruplet of substances bares resemblance to what we call the four phases of matter: solid, liquid, gas, and plasma. The phases are the different forms or properties material substances can take.

Water in two states: liquid (including the clouds), and solid (ice). Image via Wikipedia.

Compare Empedocles’ substances to the modern phases of matter. “Earth” would be solid. The dirt on the ground is in a solid phase of matter. Next comes water which is a liquid; water is the most common liquid on Earth. Air, something which surrounds us constantly in our atmosphere, is a gaseous form of matter.

And lastly, we come to fire. Fire has fascinated human beings for time beyond history. Fire is similar to plasma in that both generate electromagnetic radiation such as light. Most flames you see in your everyday life are not hot enough to be considered plasma. They are typically considered gaseous. A prime example of an area where plasma is formed is the sun. The ancient four elements have an intriguing correspondent in modern science.

Ancient Concept of Dome Sky vs. Simulation Hypothesis

Millennia ago, people held the notion that his world was flat. Picture a horizontal cooking sheet with a transparent glass bowl set on top of it. Primitive people thought of the Earth in much the same way. They considered the land itself as flat and the sky as a dome. However, early Greek philosophers such as Pythagoras (c. 570-495 BC) — who is also known for formulating the Pythagorean theorem — understood that Earth was actually spherical.

Fast forward to the 21st century. Now scientists are considering the scientific concept of the dome once again but in a much more complex manner.

Regardless of what conspiracy lovers would have you believe, the human race has ventured into outer space, leaving the face of the Earth to travel to the stars. In the face of all our achievements, some scientists actually question if reality is real, a mindboggling and apparently laughable idea.

But some scientists have wondered if we could be existing in a computer simulation. The gap between science and science fiction starts to become very fine when considering this.

This idea calls to mind classic sci-fi plots such as those frequently played out in The Twilight Zone in which everything the characters take as real turns out to be something entirely unexpected. You might also remember the sequence in Men in Black in which the audience sees that the entire universe is inside an alien marble. Bill Nye even uses the dome as an example in discussing hypothetical virtual reality. This gives one the feeling that he is living in a snowglobe.

Medieval Alchemy vs. Modern Chemistry

The alchemists of the Middle Ages attempted to prove that matter could be transformed from one object into an entirely new object. One of their fondest goals they wished to achieve was the creation of gold from a less valuable substance. They were dreaming big, but such dreams have not yet come to fruition. Could it actually be possible to alter one type of matter into another?

Well, modern chemists may be well on their way to achieving this feat some day. They are pursuing the idea of converting light into matter, as is expressed in Albert Einstein’s famous equation. Since 2014, scientists have been claiming that such an operation would be quite feasible, especially with extant technology.

Einstein’s famous equation.

Light is made up of photons, and a contraption capable of performing the conversion has been dubbed “photon-photon collider.” Though we might not be able to transform matter into other matter in the near future, it looks like the light-to-matter transformation has a bright outlook.