By Antonio Zamora and Elena M. Zamora, Updated by Lewis Loflin
Originally Published 1998, Updated March 26, 2025
The scientific method is the bedrock of modern science—a systematic process for making observations, recording data, and analyzing results in a way that other scientists can replicate. It employs two key reasoning approaches to pursue objective truth: inductive reasoning, which builds general hypotheses from specific observations, and deductive reasoning, which uses theories to predict specific outcomes.
For a subject to be studied scientifically, it must be observable and reproducible. Observations can be made with tools ranging from the unaided eye to advanced instruments like microscopes, telescopes, or voltmeters. In 1609, Galileo used the newly invented telescope to discover Jupiter’s moons, a finding confirmed by others that revolutionized astronomy. Conversely, Percival Lowell’s 1890s claim of Martian canals—attributed to intelligent life—failed the reproducibility test. Larger telescopes and NASA’s Mars missions, such as the 1976 Viking landings, found no canals, showing the importance of independent verification.
Lowell, however, demonstrated deductive reasoning’s power by predicting Pluto’s existence in 1905. He noted perturbations in Uranus and Neptune’s orbits, suggesting an unseen planet. Pluto was discovered in 1930, validating his use of gravitational theory to explain discrepancies—though Pluto’s 2006 reclassification as a dwarf planet reflects science’s evolving nature.
Scientific observations require instruments grounded in established principles. A telescope, for example, uses light refraction through lenses to magnify images, a process that can be mathematically verified to ensure the observed image matches reality. This reliability contrasts with pseudoscientific tools like divining rods, Y-shaped branches claimed to detect underground water. Experiments—such as placing a divining rod on a scale over water or dry land—show no measurable force, revealing the rod’s movement as operator-induced, not scientific.
The scientific method follows a clear cycle: 1) observe phenomena, 2) form a theory to explain the observations, 3) make predictions based on the theory, and 4) test those predictions with further observations. If predictions fail, the theory must be revised. This process demands testability—untestable theories don’t qualify as scientific. Fields like gravitation, electricity, magnetism, optics, and chemistry all rely on this approach. When competing theories arise, experiments can distinguish them, as seen in the 17th-century debate over whether light was a particle or a wave.
The particle-wave debate evolved dramatically in the 20th century. Max Planck’s 1900 quantum hypothesis proposed that energy is emitted or absorbed in discrete packets called quanta, supporting the particle theory of light. Einstein’s 1905 work on the photoelectric effect furthered this view, showing light behaving as particles. Yet, diffraction experiments with electrons—particles with measurable mass—revealed wave-like behavior. In 1926, Erwin Schrödinger’s wave equation birthed quantum mechanics, describing matter’s dual nature. Today, quantum technologies like quantum computing rely on this understanding, with companies like IBM and Google achieving quantum supremacy milestones by 2019.
Another counterintuitive truth is the speed of light’s constancy. The 1887 Michelson-Morley experiment showed light’s speed in a vacuum—299,792 kilometers per second—remains unchanged regardless of Earth’s motion. Einstein’s 1905 theory of special relativity built on this, asserting that light’s speed is constant for all observers, whether a train’s headlight approaches or recedes. Over a century of experiments, including GPS technology’s reliance on relativistic corrections, confirms this principle.
Science excels at studying isolated problems, yielding approximate solutions, but it struggles with phenomena outside these boundaries. Kurt Gödel’s 1931 incompleteness theorems proved that no formal system can prove all true statements, forcing science to occasionally discard old frameworks. Newton’s gravity, sufficient for everyday use, fails for cosmic phenomena, replaced by Einstein’s general relativity, which describes gravity as the curvature of four-dimensional spacetime.
Physical limits also constrain science. The Heisenberg Uncertainty Principle (1927) states that an elementary particle’s position and momentum cannot be measured simultaneously with precision—if one is known, the other becomes uncertain. Jacob Bronowski argued that nature isn’t fully formalizable; any system we devise excludes parts of reality, preventing a complete model of the universe.
The scientific method applies only to observable, measurable, reproducible events. It can handle statistical phenomena, like predicting radioactive decay rates in atomic chemistry, but not irreproducible events. For example, a 1990s incident where car alarms triggered simultaneously baffled engineers—lacking reproducibility, the cause (possibly radio interference) couldn’t be confirmed. Subjective experiences, like determining “great” art or comparing Picasso to Matisse, also lie beyond science, despite advances in neuroimaging that map brain activity during tasks.
So-called miracles defy scientific study due to their irreproducibility. If a terminal cancer patient’s tumors vanish, was it diet, mindset, or something else? Retrospective analysis can’t control all variables, and ethical constraints prevent experimental replication.
Since this article’s original 1998 publication, the scientific method has driven breakthroughs like the 2012 discovery of the Higgs boson at CERN, confirming the Standard Model of particle physics, and the 2015 detection of gravitational waves by LIGO, validating Einstein’s predictions. Yet, challenges persist—climate models, often untestable due to long timescales, highlight the need for rigorous adherence to testability and reproducibility, as emphasized in my Scientific Method and Its Misuse in Public Policy.
The scientific method remains a powerful tool for truth, but its limits remind us to approach claims with humility and skepticism, ensuring theories are testable and data-driven.
Acknowledgment: Originally authored by Antonio and Elena Zamora (1998). Updated by Lewis Loflin with assistance from Grok, an AI by xAI, to reflect modern examples and insights.
John Nelson Darby
Christian Premillennialism