Types of hypotheses
What are the types of hypotheses?
A hypothesis is a probable, objective and specific answer to a scientific question, which must be tested.
There are different types of hypotheses: the research or work hypothesis, the alternative hypothesis, the null hypothesis, or the statistical hypothesis.
However, it is possible for an investigation to have more than one hypothesis. This means that the different types of hypotheses are also related to each other. For example, a research hypothesis can act as the main hypothesis in a work, but in turn the null, alternative and statistical hypotheses help to clarify the central hypothesis.
To better understand this, let's look at each type of hypothesis separately and their respective variants (with examples).
Research or work hypothesis
The research hypothesis tries to answer what is the relationship that is established between various variables. It is also known as a working hypothesis. It is the starting point of all scientific research.
According to its approach, it is divided into descriptive hypotheses, causal hypotheses, correlational hypotheses or group difference hypotheses.
They limit themselves to describing the relationship between the variables under study, but they do not explain their causes. They anticipate the expected variable type, value, and qualities.
For example, "Crime in the city of Caracas has increased 50% in relation to the year 2019."
Causal hypotheses or hypothesis of causality are those that propose to explain the cause-effect relationship between two or more variables. They can be explanatory or predictive.
- Explanatory hypotheses. They offer a possible explanation for the cause that relates the variables. For example, "Excessive alcohol consumption causes neuronal damage."
- Predictive hypotheses. They predict how one variable will behave in response to another. For example, "Global warming will cause floods in the next few years."
Both explanatory and predictive hypotheses can be formulated inductively or deductively. Let's see.
- Deductive hypotheses: from a theory, the researcher formulates a hypothesis to explain a specific case. That is, deductive hypotheses are formulated from the general to the particular. For example, “All living things have DNA. Bacteria are living things. So bacteria have DNA. '
- Inductive hypotheses: from the observation of a specific case or phenomenon, the researcher formulates a generalization or general principle. That is, inductive hypotheses are formulated from the particular to the general.
For example, Newton observed that although the Moon and the apple are two spherical bodies, only the apple falls to the ground. Capturing this specific difference allowed him to induce the existence of a law that would explain such behavior. Thus, he hypothesized that there is a force of attraction (gravity) between bodies.
Correlational or joint variation hypotheses are those that establish the degree of mutual relationship between the variables, that is, how and to what degree one affects the other (and vice versa). In this type of hypothesis, the order of the variables is indifferent.
For example, Newton's theory of gravity is a correlational hypothesis, since its statement dictates: "The greater the mass, the greater the force of attraction." Correlationally, it follows that: "The greater the force of attraction, the greater the mass."
Correlational hypotheses can be negative, positive, or mixed.
- Positive: "The greater the impunity, the greater the crime."
- Negative: "The lower the fat intake, the lower the risk of coronary heart disease."
- Mixed: "The higher the altitude, the lower the temperature."
Group difference hypothesis
The group difference hypotheses are those that anticipate the difference in the behavior of various groups. It is based on statistical comparison. The group difference hypotheses are expressed in two variants:
- Those that establish a difference between two groups, without determining on which group it falls. For example, "There is a difference in mortality rates from covid19 between female and male people."
- Those that determine on which of the groups the difference falls. For example, "The death rate from covid19 is higher in males than females".
The null hypothesis is one that denies the relationship between two or more variables based on a sample parameter. Your statement is negative, which means that it includes a "no." It is represented by the symbol H0. The null hypothesis is not accepted, but is rejected or not rejected.
The formulation of the null hypothesis is normally accompanied by the formulation of an alternative hypothesis that attempts to prove its falsehood.
For example, "Muscle mass index is not associated with people's sex."
Every null hypothesis generates an alternative hypothesis, that is, an alternative response to the null hypothesis that tries to prove its falsehood. It is represented by the symbol H1. This type of hypothesis is accepted or not accepted.
- H0: «The muscle mass index is not associated with the sex of the people»
- H1: "The muscle mass index differs between men and women."
Statistical hypotheses are those that translate the hypotheses into statistical symbols. Seek to affirm or define the parameters of one or more populations. Therefore, they are formulated whenever it is expected to collect data in numbers, percentages or averages.
They are subdivided into:
- estimation hypotheses, which deal with descriptive hypotheses of a single variable. This is analyzed in context. The researcher formulates a statistical estimate of the result.
- statistical correlation hypotheses, which deal with correlation hypotheses, which are those that study the relationship between two or more variables.
- statistical hypotheses of mean differences, which deal with the difference of groups. Compare the numerical estimates between two or more groups in analysis.
It may interest you:
- What is a hypothesis?
- Examples of hypotheses
- Scientific investigation
- Types of research