Biology Theory vs Hypothesis; Evolution, Phylogeny, and Null Hypothesis

Theories vs. Hypotheses

  • Theories unify explanations and are supported by evidence; hypotheses are testable predictions derived from theories.
  • Theories form the basis for hypotheses; hypotheses test aspects of a theory.

Core biological theories

  • Biology’s two foundational theories: cell theory and evolution.
  • Chemistry’s foundations lean on atomic theory; biology relies on cell theory and evolution.

Evolution: testable predictions (example: leafy sea dragon)

  • Theory: leafy sea dragons camouflage to reduce predation (an evolutionary explanation).
  • Predator senses to consider: vision within the visible spectrum (not just UV/IR).
  • Predictions from the hypothesis:
    • Seaweed/kelp in their habitat should resemble the dragons’ coloration and pattern (to avoid detection).
    • If sea dragons are experimentally painted different colors, survival may change due to predation risk.
  • Purpose: theory provides testable predictions to validate or falsify the hypothesis.

Phylogenetics and evidence for relationships

  • Phylogenetic trees summarize hypotheses about relationships among organisms.
  • Evidence to build trees:
    • DNA or protein sequence similarity (closer sequences → closer relationships).
    • Anatomical and developmental traits (e.g., cellular properties of humans and animals).
  • Humans (and primates) are classified as animals partly due to
    • cellular similarities with other animals; early embryonic traits (e.g., gill slits) illustrate shared ancestry.

Mating displays and null hypotheses

  • Mating display hypotheses can have multiple outcomes (some displays attract, some repel).
  • Null hypothesis for mating display: the display has no effect on mating success/attraction.
  • In science, we aim to reject the null hypothesis to support the alternative hypothesis.

Null hypothesis and statistical testing

  • Null hypothesis (H0H_0): e.g., a drug has no impact on long-term cancer survival.
  • The goal of testing: reject H<em>0H<em>0; doing so provides support for the alternative hypothesis H</em>1H</em>1, but does not prove it absolutely.
  • Statistical analysis never proves; it only rejects or fails to reject a hypothesis.
  • In practice, multiple tests over time build a case (not a proof), akin to a gradual accumulation of evidence.
  • Analogy: proof by contradiction parallels rejecting H0H_0 to refuting a claim; failure to reject invites further testing.

Closing note

  • We’ll explore how these ideas apply in the lab next week.