A hypothesis is a tentative idea, concept or explanation that researchers & analysts predict will be the most likely outcome. Hypothesizing is an integral aspect of the scientific method and is crucial for analyzing any natural or artificial phenomenon. Careful observation and investigation via inductive reasoning precede the formulation of a hypothesis. Then, rigorous experimentation and careful analysis & interpretation of resultant data either establish or refute the hypothesis.
The difference between hypothesis and theory underlines a researcher’s efforts to identify & understand any natural or man-made phenomenon. In the scientific research process, a hypothesis is the proposed answer to a research question, and theories are hypotheses that experimental observations and data have substantiated. Hypotheses and theories are, thus, the heart of any scientific experimentation.
Let us try to understand the role of hypothesis & theory in the scientific research method better.
Hypothesis vs Theory In Science
When it comes to scientific research, the term ‘theory’ is often closely associated with the terms ‘laws’ & ‘facts’. Are scientific laws and facts derived from theories? What is the difference between hypothesis, theory and law?
In any scientific article, you will find the words ‘hypothesis’, ‘theory’, and ‘law’ used to explain and identify something. What do they all mean, and how do they differ?
A simple way to define a hypothesis can be as follows.
A hypothesis is a reasonable guess a researcher makes based on what they know or observe.
In the scientific world, hypotheses are proven and disproven all the time. Therefore, hypotheses play a critical role in the scientific method. Researchers formulate a research question, develop a hypothesis, make a testable prediction, test & experiment and then analyse the results.
A hypothesis needs to be tested and retested time & again before it can be accepted as a theory.
Here’s a highly relatable example of the hypothesising process for any layman — > Every morning, you wake up, and you find the trashcan in your backyard overturned, with trash spread all over the yards. You form a hypothesis that raccoons are responsible. Further observation is necessary to support or refute this hypothesis.
Scientific theories consist of one or more hypotheses that have been supported via thorough, repeated testing. Through continuous experimentation, logical reasoning & deductions and careful analyses, hypotheses are transformed with theories, which are then widely accepted in the scientific community.
Hypothesis, alongside testable predictions and observations, are used to further test and retest theories. Rigorous testing and experimentation on existing ideas lead to hypotheses that can reinforce or overturn the existing theories.
Laws are testable theories that have been proven time and again. They have been proven to be accurate and can’t be proven otherwise. Laws or established theories are universal and cornerstones of science.
Theory vs Difference: What’s The Key Difference?
A hypothesis proposes a tentative explanation, solution, or prediction for some phenomenon in scientific research. On the other hand, A theory is a substantiated hypothesis that has been proven with rigorous experimental data & observations. Theories rely on tested and verified data and are widely accepted to be true.
Hypothesis testing is crucial for supporting or rejecting inductions made via careful observations and logical reasoning.
Testing A Hypothesis
One of the fundamental requirements of any hypothesis is that it should be testable against reality and eventually supported or rejected. Testing a hypothesis requires researchers to propose and test a possible relationship between an independent variable (what the researcher changes) and a dependent variable (what the researcher observes).
Two kinds of hypotheses find a place in research convention: the null hypothesis and the alternative or the research hypothesis.
- Alternative or Research Hypothesis: The alternative hypothesis states a relationship between the variables to be studied. An experimental or research hypothesis predicts changes in the dependent variable when the independent variable is manipulated.
Alternative hypotheses play a crucial role in supporting the investigated theory or the studied phenomenon.
Null Hypothesis: Null hypothesis claims no relationship between the variables under study. They do not affect each other, and changes in the dependent variable will not manipulate the independent variable.
Non-directional Hypothesis: A non-directional or two-tailed hypothesis states that the independent variables will affect the dependent variables, but the details of the direction of the effect are not specified.
Directional Hypothesis: These hypotheses predict both the nature of the effect of variable changes and the direction/magnitude of the change.
Tests of hypotheses: Tests of hypotheses attempt to reject the NULL HYPOTHESIS. The null hypothesis is tested against the analysis of data collected. Thresholds of acceptance and rejection are generally determined as per research goals and the behaviour of the factors involved.
The ALTERNATIVE HYPOTHESIS is accepted when the null hypothesis is proven to be false.
Hypotheses are the basis of any investigative inference that depends upon inductive reasoning & subsequent experimentation & observation. Thus, besides scientific research, hypothesizing also has applications in the field of statistical inference.
Crafting A Hypothesis
Writing a well-rounded research hypothesis requires acute observation, deep understanding and intuitive analysis of the phenomenon under study. Below is the essential roadmap for crafting a focused research hypothesis.
Steps To A Hypothesis
1. Developing a perfect alternative and null hypotheses for an investigation requires careful identification of the critical variables involved in the process. Then, the researcher manipulates the independent variable, and the dependent variable is the measured outcome.
The next step is the operationalization of the variables in the hypothesis
2. The operationalization of a hypothesis is the process of making the variables measurable or testable. For example, if you intend to study the spread of infectious disease among a specific demographic, you need to ascertain the variables that affect the spread of the infection.
3. Decide upon a direction for your prediction. If the evidence in related literature supports a particular effect of the independent variable on the dependent variable, write a directional (or a one-tailed) hypothesis.
If there are limited or ambiguous evidence regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
4. Write down the hypothesis concisely and crisply. Use clear and straightforward language and mention the variables and the phenomenon precisely.
Let’s round up with a look at the critical differences between scientific and statistical hypotheses.
Scientific vs Statistical Hypothesis
The ideas behind developing a hypothesis and eventual inference depend upon observation and inductive reasoning. & The case remains the same no matter what the applicational domain or use case.
Scientific hypotheses that need to be tested statistically are generally categorized as statistical hypotheses. A researchers’ main goal for statistical hypothesis testing is to determine whether the collated data support certain statements or predictions.
Like scientific hypotheses, statistical hypotheses are also conjectures about population parameters. Such conjectures may or may not be valid.
Statistical tests attempt to reject the NULL HYPOTHESIS. The null hypothesis is the statement being tested, and the ALTERNATIVE HYPOTHESIS is accepted when the null hypothesis is proven to be false.
NULL HYPOTHESIS (H0) in statistical testing must contain a condition of equality =, >= or<=
ALTERNATIVE HYPOTHESES must be true if H0 is false.
If the probability of a particular observed difference is below some predetermined value, known as the significance level, the Null Hypothesis is rejected, and the result is deemed statistically significant.
That’s all the space we have for today. Hope this article was informative enough to understand the differences between theory & hypothesis and learn the nuances of hypothesis testing in science & statistics.
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