# Types of Hypotheses in scientific research (and examples)

**There are different types of hypothesis in scientific research** . From the null, general or theoretical hypothesis, to the complementary, alternative or work hypothesis.

- Related article: "The 15 types of research (and their characteristics)"

## What is a hypothesis?

But, **What exactly is a hypothesis and what is it for?** The hypotheses specify the possible characteristics and results that may exist between certain variables that are going to be studied.

Through the scientific method, a researcher should try to verify the validity of his initial (or main) hypothesis. It is what is usually called work hypothesis. At other times, the researcher has several complementary or alternative hypotheses in mind.

If we examine these working hypotheses and alternatives we find three subtypes: attributive, causal and associative hypotheses. The general or theoretical hypotheses serve to establish a relationship (negative or positive) between the variables, while the work hypothesis and alternatives are those that effectively quantify this relationship.

On the other hand, the null hypothesis reflects the fact that there is no appreciable link between the variables studied. In the case in which it can not be verified that the working hypothesis and the alternative hypothesis are valid, the null hypothesis is accepted as correct.

Although the aforementioned are considered the most common types of hypotheses, there are also relative and conditional hypotheses. In this article we will discover all types of hypotheses, and how they are used in scientific investigations.

## What are the hypotheses for?

**Any scientific study must be initiated taking into account one or more hypotheses** that is intended to confirm or disprove.

A hypothesis is no more than a conjecture that can be confirmed, or not, by a scientific study. In other words, hypotheses are the way that scientists have to pose the problem, establishing possible relationships between variables.

## Types of hypotheses used in a scientific study

There are several criteria that can be followed when classifying the types of hypotheses used in science. We will know them below.

### 1. Null hypothesis

**The null hypothesis refers to that there is no relationship between the variables that have been the subject of research** . It is also called a "no relationship hypothesis", but it should not be confused with a negative or inverse relationship. Simply, the variables studied seem to follow no concrete pattern.

The null hypothesis is accepted if the scientific study results in the hypothesis of work and alternatives not being observed.

#### Example

"There is no relationship between people's sexual orientation and their purchasing power."

### 2. General or theoretical hypotheses

**The general or theoretical hypotheses are those that scientists establish prior to the study and conceptually** , without quantifying the variables. Generally, the theoretical hypothesis is born of generalization processes through certain preliminary observations about the phenomenon that they wish to study.

#### Example

"The higher the level of studies, the higher the salary". There are several subtypes within the theoretical hypotheses. The difference hypotheses, for example, specify that there is a difference between two variables, but they do not measure their intensity or magnitude. Example: "In the faculty of Psychology there is a greater number of students than of students".

### 3. Work hypothesis

**The working hypothesis is the one used to try to demonstrate a concrete relationship between variables** through a scientific study. These hypotheses are verified or refuted by means of the scientific method, so sometimes they are also known as "operational hypotheses". Generally, working hypotheses arise from deduction: based on certain general principles, the researcher assumes certain characteristics of a particular case. The working hypotheses have several subtypes: associative, attributive and causal.

#### 3.1. Associative

The associative hypothesis specifies a relationship between two variables. In this case, if we know the value of the first variable, we can predict the value of the second variable.

##### Example

"There are twice as many students enrolled in the first year of high school than in the second year of high school."

#### 3.2. Attributable

The attributive hypothesis is the one used to describe the events that occur between the variables. It is used to explain and describe real and measurable phenomena.This type of hypothesis contains only one variable.

##### Example

"The majority of homeless people are between 50 and 64 years old."

#### 3.3. Causal

The causal hypothesis establishes a relationship between two variables. When one of the two variables increases or decreases, the other one increases or decreases. Therefore, the causal hypothesis establishes a cause-effect relationship between the variables studied. To identify a causal hypothesis, a causal link, or statistical (or probabilistic) relationship must be established. It is also possible to verify this relationship through the refutation of alternative explanations. These hypotheses follow the premise: "If X, then Y".

##### Example

"If a player trains an extra hour each day, his percentage of success in the throws increases by 10%."

### 4. Alternative hypotheses

**The alternative hypotheses try to offer an answer to the same question as the working hypotheses** . However, and as can be deduced by its denomination, the alternative hypothesis explores different relationships and explanations. In this way it is possible to investigate different hypotheses during the course of the same scientific study. This type of hypothesis can also be subdivided into attributive, associative and causal.

## More types of hypothesis used in science

There are other types of not so common hypotheses, but they are also used in different types of investigations. They are the following.

### 5. Relative hypotheses

**The relative hypotheses give evidence of the influence of two or more variables** on another variable.

#### Example

"The effect of the decline in per capita GDP on the number of people who have private pension plans is less than the effect of the fall in public spending on the rate of child malnutrition."

- Variable 1: decrease in GDP
- Variable 2: drop in public spending
- Dependent variable: number of people who have a private pension plan

### 6. Conditional hypotheses

**Conditional hypotheses serve to indicate that one variable depends on the value of two others** . It is a type of hypothesis very similar to the causal ones, but in this case there are two variables "cause" and only one variable "effect".

#### Example

"If the player receives a yellow card and is also warned by the fourth referee, he must be excluded from the game for 5 minutes."

- Cause 1: receive a yellow card
- Cause 2: be warned
- Effect: be excluded from the game for 5 minutes. As we can see, for the variable "effect" to occur, it is not only necessary to fulfill one of the two variables "cause", but both.

## Other classes of hypothesis

The types of hypotheses that we have explained are the most commonly used in scientific and academic research. However, they can also be classified based on other parameters.

### 7. Probabilistic hypotheses

**This type of hypothesis indicates that there is a probable relationship between two variables** . That is, the relationship is fulfilled in most cases studied.

#### Example

"If the student does not spend 10 hours a day reading, (probably) will not pass the course."

### 8. Deterministic hypotheses

**Deterministic hypotheses indicate relationships between variables that are always met** , without exception.

#### Example

"If a player does not wear taco boots, he can not play the game."

#### Bibliographic references:

- Hernández, R., Fernández, C., and Baptista, M.P. (2010) Research Methodology (5th Ed.). Mexico: McGraw Hill Education
- Salkind, N.J. (1999). Research Methods. Mexico: Prentice Hall.
- Santisteban, C. and Alvarado, J.M. (2001). Psychometric models. Madrid: UNED