It is vital to fully understand a hypothesis to address the types of research hypotheses. A hypothesis explains an established or known fact that has not yet been proven or validated.

Simply put, it is a statement explaining why and how a particular thing works based on philosophical assumptions and facts.

For example, a hypothesis goes like this;

*A patient is likely to trust the pediatrician’s diagnosis based on the perception that the doctor is well-versed in the practice of medicine.*

A hypothesis is a basis for scientific research or experiment, usually coined as a research hypothesis.

Three attributes or features measure the viability of a research hypothesis, and they are as follows.

- A research hypothesis must be specific, testable or measurable, and verifiable. In other words, the research hypothesis should create clear predictions than can be tested.
- Ideally, a hypothesis can be drawn from previous theoretical research publications.
- A good research hypothesis is much more than an intelligent guess, and sometimes, a research hypothesis could take the form of research questions that can be explored further via research and suggest an expected result.

Research hypotheses are a vital part of the scientific process that leads to or are the reasons for scientific experiments. That said, a slight flaw in constructing a hypothesis could generate negative results.

There are various types of hypotheses, and the following checklist should guide a good hypothesis.

- Is the language employed clear and direct?
- Is there a good relationship between the hypothesis and the research topic?
- Can the hypothesis be tested?
- What are the methods used to carry out testability?
- What are areas of explanation being addressed?

The essence of this checklist is to get your hypothesis up on the right footing and help you pinpoint any gaps or weaknesses.

The following listed below are the various 7 types of research hypotheses.

It can show the impact of a relationship between a single **dependent** variable and a single **independent** variable. For example,

*Consuming too many fizzy drinks will cause weight gain and a bloated belly.*

It foretells the relationship between multiple independent and dependent variables.

For instance, eating more vegetables and a low-calorie diet would lead to weight loss.

It shows the expected direction required to determine the relationship between variables and is derived from theory. Furthermore, it shows a researcher’s intellectual commitment to a particular outcome by the length of the study.

For example,

*Toddlers under the age of 4 who were given well-balanced meals for 5 years showed a higher IQ level than their counterparts who did not have the same treatments.*

It does not predict the direction or nature of the relationship between the two variables. A non-directional hypothesis is used mostly when there is no theory involved. For example, men and women differ in terms of helpfulness.

The associative hypothesis shows the interdependency between related variables. A change in one variable results will cause a change in the other variable. However, the change is not caused by either of the variables.

For example, the increase in the number of unhealthy people visiting a particular hospital is not because the hospital is the source of their illness. Rather it could be a result of other unrelated factors like the weather, personal hygiene practices, etc.

On the other hand, the causal hypothesis predicts the effect a change in variables would have on different variables. For instance, a change in the writing style on their blog led to higher user engagement.

This refers to a lack of relationship between different variables. For example, plants would grow irrespective of the source of water, natural or artificial. It proposes a negative statement to support the researcher’s discovery, showing that no relationship exists between the two variables.

The alternative hypothesis is a statement used in statistical experiments. It is the opposite of the null hypothesis and is described by the term H1 or Ha. The term alternative is used because it is the alternative to the null hypothesis. Therefore it is safe to say that it is an alternative theory to the one a researcher is testing and trying to prove.

The Alternative Hypothesis is classified into two categories;

Directional and Non-Directional.

**Directional:**A statement outlining the ways the expected outcomes would be collated. It is mostly used in cases where there is a need to establish a relationship between two different things or when comparing various groups. For example, Attending physiotherapy sessions will improve the stage performance of ballerinas.

**Non-directional:**This implies no direction for the expected results. For example, attending physiotherapy sessions impacts the stage performance of ballerinas.

The directional statement clearly states that the physiotherapy sessions would boost performance in both examples outlined above. At the same time, the non-directional only acknowledges that the sessions would influence performance without stating whether the influence would be positive or negative.

When a theory is proven through an experiment and observation, this justifies or validates a claim and distinguishes it from a wild guess.

Here are a few examples that depict the empirical hypothesis:

*a. Women who take folate supplements face a lesser risk of having children with congenital disabilities. *

*b. Good behavior in children can be reinforced when they are rewarded for good behavior.*

It is a statement that postulates a theory based on studying a sample population. It is a logic-based analysis where a specific population is researched to gather evidence to prove a particular theory.

For example:

*43% of the American population in the age group of 22-29 speak a second language.*

Testability in the hypothesis is crucial in establishing any scientific research in the physical world. This is because research or any science founded on a hypothesis is usually laced with inherent flaws. One of the flaws is the idea that any hypothesis by design significantly reduces the area of exploration, which births experimental results that would fail in real-life scenarios.

This problem is further compounded by modern science, which equates philosophical concepts to physical science. Testability solves these problems by making the research hypothesis more truthful, based on real tenable results. Hence any well-thought-out hypothesis would be founded in testability.

The condition for any viable hypothesis is testability. To be considered testable, the following criteria must be fulfilled.

- There must exist a viable means to prove that the hypothesis is true.
- Similarly, there must be a possibility to prove the hypothesis false.
- Finally, the result of the hypothesis must be replicable.

Without these testability criteria, the hypothesis and proposed results would be indefinite, and the significance of the experiment would be lost.

There are clear and precise steps to creating an effective research hypothesis. An effective research hypothesis must answer these 6 questions;

**Step 1**

What, who, where, when, how, and why?.

In the scientific method, the first step is to ask a question. Frame this question using the classic six highlighted above. For example:

- How long does it take avocados to grow?
- Why do we have shorter days and longer nights in winter?
- What happened to the groundnut pyramids?
- How does a caterpillar become a butterfly?
- Why are students excited on Friday afternoon?
- How does sleep affect motivation?
- Why do tax systems help build an economy?

So the first step is to identify and state what problem you are trying to solve. The hypothesis must clearly define the subject, the experiment’s focus, and the expected outcome.

**Step 2**

Put together preliminary research data from a wide range of sources, including academic journals, personal experiments, and observations from the work of others. Afterward, define the variables, and separate the dependent variables from the independent variables.

The independent variables are the ones that are malleable and can be tweaked, controlled, changed, and affected by various conditions. Secondly, independent variables are isolated from other factors of the research.

On the other hand, dependent variables rely on other aspects of the research and are affected by any change in the independent variable.

**Step 3**

Refine your hypothesis by emulating the following as a checklist:

- Specific language devoid of any ambiguity must be used.
- Clearly predict the relationship between the variables and the expected outcome.
- No prior assumptions should be made about the reader’s knowledge.
- The results must be testable, relevant, and specific to the research questions.

However, one of the proven methods of determining the effectiveness of your research hypothesis is to compare it to an already-existing hypothesis. It would help guide and make the process easier.

Here are a few general examples that can guide you in formulating your hypothesis:

a. *Eating a generous amount of fiber-rich fruits like apples after age 50 would keep the doctor away or limit visits to the doctor’s office.*

*b. Cheap airlines, referred to as budget airlines, will receive more customer complaints than regular or premium airlines because of the limited amenities provided compared to full-service airlines.*

**Step 4**

Stating the obvious, the final step is to write your hypothesis using all the steps outlined. It is important to remember that your hypothesis is a statement that shows who or what is being studied, the variables, and your predicted outcome.

We have already established that a hypothesis is an idea or a statement based on tangible evidence that can be proven. A hypothesis in research is simply a statement concerning the predicted outcome of a scientific study. In this instance, it has to be specific, testable, and falsifiable.

**Specific** here refers to clarity about the parties involved and the expected results.

For example, a patient’s perception of a doctor’s experience breeds a higher level of trust in the doctor’s diagnosis.

This example depicts the clarity and directness of the subject. There is no ambiguity in the expectations of the relationship referred to.

**Testability** in research hypothesis is simply saying that the hypothesis must be provable. This means that the data gathered must be collected and observed in a thorough scientific process to assess the quality of the hypothesis. In other words, there must be a proven way to validate the claims of the hypothesis.

For example, the doctor referred to in the previous hypothesis can be validated by other patients’ perceptions of his competence and previous results from past diagnoses. A quantitative research approach using a large number of people would have been used to test the claims of this hypothesis.

The **falsifiability** in the research hypothesis means that the hypothesis can be refuted. This step is essential in validating or establishing the viability of the hypothesis. Hence there has to be an emphatic way of confirming if a hypothesis is false.

The claim is that life exists on planets like the earth. This claim cannot be a hypothesis because the only way to verify this would be to visit all planets in the world and come back with evidence of life. This claim is not disprovable.

So when conducting a hypothesis in research, it is vital to meet all these criteria to have an effective hypothesis.

A hypothesis in statistics is a legal claim about a subject within the framework of a statistical model. It is a process of statistical inference to determine if the data collated is inadequate to prove a hypothesis. The data used here can be gleaned from a large population. A statistical analyst verifies a hypothesis by analyzing a random sample of the population.

In this case, the random population sample is used to test 2 different hypotheses; the null hypothesis and the alternative hypothesis.

There is a four-step process used for statistical hypothesis testing.

- State only two hypotheses; that way, only one can be right.
- Create an analysis plan that shows how the data would be evaluated.
- Implement the plan by physically analyzing the sample data
- Analyze the result and either accept the hypothesis or state the plausible hypothesis based on the given data.

For example, if you want to carry a test on, say, 50% of exceptional college students come from wealthy homes.

The null hypothesis would be that 50% of the students are from wealthy homes, while the alternative hypothesis would be that 50% of the students are not from wealthy homes.

A random sample of 100 students in the said college would be carried out via a survey, and the null hypothesis would be tested.

If 40 of those students are not from wealthy homes, then the 50% null hypothesis would be rejected, and the alternative hypothesis would be accepted.

In the scientific hypothesis, the researcher’s expectation from the experiment is achieved following a scientific method outlined below:

- Create the question
- Carry out a background research
- Creating a hypothesis
- Design an experiment
- Collect data
- Analyze the results
- Reach a conclusion
- Share the results

In the scientific hypothesis, the statement is a prediction; then, it evolves into a question, answered via research. It is at the point the hypothesis states the desired expectation. The next step after this is to test the hypothesis.

For example, the effect of Vitamin C supplements for a patient with cold symptoms is that the medication would help alleviate the effects of the cold.

As we established, a hypothesis predicts a relationship between variables that is yet to be proven. Creating a viable research hypothesis involves conducting research and broadening your knowledge about the subject via studying in other to choose the area of focus. Different types of hypotheses can be adopted to validate your predictions. The hypothesis should be testable in other to validate the claims.

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