Hypothesis Generation
Hypothesis Generation - Related Videos
05:10
Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Cited by 4 •
2016
Here we describe a protocol using the web-based drug repurposing hypothesis generation tool: "RE:fine Drugs." This protocol can be modified to a user's preferences at the level of the query type (gene, drug or disease) and/or the range of available advanced options.
01:14
What is a Hypothesis?
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2023
A hypothesis can be a simple sentence or statement about a property or any phenomenon observed or predicted for a population. It is usually a claim about a property of the population. It can be stated for any field observations or experiments. A hypothesis statement cannot be said to be right or wrong as it is merely a statement. It needs to be tested through an elaborate data collection process and an appropriate statistical test. A hypothesis should be a general but not a vague statement. It...
01:11
Types of Hypothesis Testing
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2023
There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed. When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
01:14
Errors In Hypothesis Tests
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2023
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not. The decision is not to reject null hypothesis when it is true (correct decision). The decision is to reject the null hypothesis when it is true (incorrect decision known as a Type I error). The decision is not to reject the null hypothesis when, in fact, it is false (incorrect decision known as a Type II error). The...
01:16
Hypothesis Test for Test of Independence
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2023
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are: H0: The two variables (factors)...
FAQs
The process connects an observation to a provisional explanation and then asks what should be detectable if that explanation is correct. In infection research, clinical patterns, pathogen features, immune-cell behavior, or experimental results can lead to predictions about host-pathogen interactions, immune recognition, inflammation, or resistance to treatment.
Specific predictions make a proposed explanation vulnerable to evidence rather than leaving it unfalsifiable. They identify measurable outcomes that could support, refine, or reject the model. This also helps distinguish competing explanations, because each may predict different immune responses, infection patterns, or experimental results under the same conditions.
A hypothesis should remain provisional and change when emerging data do not fit its predictions. Researchers may refine the model to account for the new evidence or reject it in favor of another explanation. Unexpected findings therefore become scientifically useful, because they can reveal limitations in current reasoning and suggest new research directions.
Researchers first identify the relevant evidence, state a provisional explanation, and derive predictions that can be measured. They then select experiments capable of testing those predictions, define appropriate controls, and specify the outcomes that would support or challenge the model. This sequence keeps experimental design connected to the original scientific question.
In immunology and infection, a proposed explanation can organize questions about how the host detects a pathogen, how immune cells behave, or how inflammation develops. Its predictions help researchers choose relevant experiments and measurable outcomes, creating a structured way to examine host-pathogen interactions rather than treating each observation as an isolated result.
It is useful when observations suggest that an infection does not respond as expected to treatment. Researchers can connect pathogen features, clinical patterns, or experimental findings to a provisional explanation for resistance, then identify predictions and controls that test it. Results can guide refinement of the model and define new directions for infection research.