A hypothesis is a tentative answer to a research question that can be tested with evidence. However, not all hypotheses are equally valid or useful. A key criterion for a good hypothesis is that it is falsifiable, which means that it can be proven wrong by empirical data. How can you determine if your hypothesis is falsifiable? Here are some tips and examples to help you.
Falsifiability is important because it allows you to evaluate your hypothesis objectively and avoid confirmation bias, which is the tendency to look for or interpret information that supports your preconceptions. If your hypothesis is falsifiable, you can design an experiment or observation that can potentially disprove it, and then compare the results with your prediction. This way, you can either reject or accept your hypothesis based on evidence, not on personal beliefs or preferences.
A falsifiable hypothesis can be disproved by evidence. For example, "aliens don't exist" is falsifiable since finding an alien disproves it. The claim "all frogs are green" is falsified by a purple frog, but altering it to exclude exceptions is naive falsifiability. Bertrand Russell's teapot, too small to be seen orbiting the Sun, exemplifies an unfalsifiable claim, as does "Aliens exist," which can't be disproven by any single observation due to the universe's vastness.
To formulate a falsifiable hypothesis, you need to make sure that it is specific, measurable, and testable. A specific hypothesis clearly defines the variables, the population, and the relationship that you are investigating. A measurable hypothesis uses operational definitions, which are precise and quantifiable ways of measuring the variables. A testable hypothesis can be verified or refuted by empirical data, which are observable and replicable facts or events.
Falsifiable hypotheses meet the criteria of specificity, measurability, and testability. For example, a hypothesis that drinking coffee increases alertness in college students is specific as it identifies the independent variable (coffee), the dependent variable (alertness), and the population (college students). It is also measurable, as it uses operational definitions for the variables such as the amount and type of coffee, the time and duration of consumption, and the indicators and scales of alertness. Moreover, it is testable by conducting an experiment with a control group and a treatment group, and measuring their alertness levels before and after drinking coffee. Similarly, a hypothesis that exposure to violent media causes aggression in children is specific, measurable, and testable. It identifies the independent variable (violent media), the dependent variable (aggression), and the population (children). It also uses operational definitions for the variables such as types and frequency of violent media, age and gender of children, and behaviors and measures of aggression. The hypothesis can be tested by conducting an observation or a survey with a sample of children, and assessing their exposure to violent media and their aggression levels.
Non-falsifiable hypotheses fail to meet the criteria of specificity, measurability, and testability. For example, the hypothesis that "love is the most powerful force in the universe" does not define what love is, how it is manifested, or what it affects. Additionally, it does not use operational definitions for the variable of love, which is subjective and abstract. Furthermore, it cannot be verified or refuted by empirical data, which are limited by time and space. Similarly, the hypothesis that "there is life on other planets" does not specify what kind of life, where, or how it is detected. It also does not use operational definitions for the variable of life, which is complex and diverse. In addition, it cannot be tested by current technology or resources, which are insufficient and inaccessible.
If your hypothesis is not falsifiable, you can try to improve it by narrowing down the scope, defining the terms, and identifying the evidence. For example, you can modify the previous hypotheses to be more specific and measurable, and thus more testable. For instance, a hypothesis about love increasing oxytocin levels in humans could be more specific by specifying what love is, how it is measured, and who it affects. It could also be more measurable by using operational definitions for the variables of love and oxytocin, which are quantifiable and observable. Furthermore, it could be made more testable by conducting an experiment with a sample of humans, and measuring their oxytocin levels before and after experiencing love. Similarly, a hypothesis about microbial life on Mars could be made more specific by specifying what kind of life, where, and how it is detected. It could also be made more measurable by using operational definitions for the variables of microbial life, organic molecules, and water, which are identifiable and measurable. Additionally, it could be made more testable by sending a rover or a probe to Mars, and analyzing the soil samples for the signs of life.
Ejemplos de hipótesis falsables en la salud, o relacionados: H1: el peso de un recién nacido de un hospital ‘X’ se verá condicionado por la alimentación de su madre. H2: el estrés laboral es ocasionado por el inadecuado mobiliario destinado para trabajar.