Types of hypotheses in scientific research (and examples)

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

    What is a hypothesis?

    But, What exactly is a hypothesis and what is it for? The hypotheses specify the characteristics and possible outcomes that may exist between certain variables that will be studied.

    Using the scientific method, a researcher should try to verify the validity of his initial (or main) hypothesis. This is often called a working hypothesis. At other times, the researcher has several complementary or alternative hypotheses in mind.

    If we examine these working and alternative hypotheses, we find three subtypes: attributive, causal and associative hypotheses. General or theoretical assumptions are used to establish a relationship (negative or positive) between variables, while working and alternative assumptions are those that effectively quantify this relationship.

    On the other hand, the null hypothesis is that which reflects the absence of appreciable link between the variables studied. In the event that it cannot be verified that the working hypotheses and the alternative hypotheses are valid, the null hypothesis is accepted as correct.

    While these are considered the most common types of assumptions, there are also relative and conditional assumptions. In this article, we will learn about all types of hypotheses, and how they are used in scientific research.

    What are the assumptions for?

    Any scientific study must be initiated taking into account one or more hypotheses which aims to confirm the rebuttal.

    A hypothesis is nothing more than a conjecture that may or may not be confirmed by scientific study. In other words, hypotheses are the way scientists approach the problem, making possible relationships between variables.

    Types of assumptions used in a scientific study

    Several criteria can be followed when classifying the types of hypotheses used in science. We will meet them below.

    1. Null hypothesis

    The null hypothesis refers to the fact that there is no relationship between the variables that have been studied. It is also called the “non-relation hypothesis”, but it should not be confused with a negative or inverse relation. Simply put, the variables studied do not seem to follow any particular pattern.

    The null hypothesis is accepted if the scientific study leads to the non-observation of the working and alternative hypotheses.

    Example

    “There is no connection between people’s sexual orientation and their purchasing power.”

    2. General or theoretical assumptions

    General or theoretical hypotheses are those that scientists establish before the study and conceptually, Without quantifying the variables. Usually, the theoretical hypothesis arises from generalization process through some preliminary observations on the phenomenon they want to study.

    Example

    “The higher the level of education, the higher you are.” There are several subtypes in theoretical hypotheses. Difference assumptions, for example, state that there is a difference between two variables, but do not measure their intensity or magnitude. Example: “In the Faculty of Psychology, there are more students than students.”

    3. Working hypothesis

    The working hypothesis is the one used to try to demonstrate a concrete relationship between the variables through a scientific study. These assumptions are verified or refuted by scientific methods, so they are sometimes referred to as “operational assumptions”. Generally, the working hypotheses follow from deduction: from 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.

    Example

    “There are twice as many people enrolled in the first year of high school than in the second year of high school.”

    3.2. Attributive

    The attributive hypothesis is the one used to describe the facts 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

    “Most of the homeless 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 undergoes an increase or a decrease. The causal hypothesis therefore establishes a cause and effect relationship between the variables studied. To identify a causal hypothesis, a cause-and-effect relationship or a statistical (or probabilistic) relationship must be established. It is also possible to verify this relationship by refuting alternative explanations. These assumptions follow the premise: “If X, then I.”

    Example

    “If a player practices 1 additional hour per day, their throwing success percentage increases by 10%.”

    4. Alternative hypotheses

    Alternative hypotheses attempt to provide an answer to the same question as the working hypotheses. However, and as can be deduced from its name, the alternative hypothesis explores different relationships and explanations. In this way, it is possible to study different hypotheses during the same scientific study. This type of hypothesis can also be subdivided into attributive, associative and causal.

    More types of hypotheses used in science

    There are other types of hypotheses that are less common, but are also used in different types of research. They are as follows.

    5. Relative assumptions

    Relative assumptions provide proof of the influence of two or more variables on another variable.

    Example

    “The effect of declining GDP per capita on the number of people benefiting from private pensions is less than the effect of declining public spending on the rate of child malnutrition.”

    • Variable 1: decrease in GDP
    • Variable 2: decrease in public spending
    • Dependent variable: number of people with a private pension plan

    6. Conditional assumptions

    The conditional hypotheses make it possible to underline that a variable depends on the value of two others. This is a type of hypothesis very similar to causal hypotheses, but in this case there are two “cause” variables and only one “effect” variable.

    Example

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

    • Cause 1: Receive a yellow card
    • Cause 2: Get notified
    • Effect: Being excluded from the game for 5 minutes. As we see, for the variable “effect” to occur, not only one of the two variables “cause” must be satisfied, but two.

    Other classes of hypotheses

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

    7. Probabilistic assumptions

    Such hypotheses indicate that there is a probable relationship between two variables. In other words, the relationship is fulfilled in most of the cases studied.

    Example

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

    8. Deterministic assumptions

    Deterministic assumptions indicate relationships between variables that are always satisfied, Without exception.

    Example

    “If a player does not wear spiked boots, he will not be able to play the game.”

    Bibliographical references:

    • Hernández, R., Fernández, C., and Baptista, MP (2010) Research Methodology (5th ed.). Mexico: McGraw Hill Education
    • Salkind, NJ (1999). Research methods. Mexico: Prentice Hall.
    • Santisteban, C. and Alvarado, JM (2001). Psychometric models. Madrid: UNED

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