Scientific studies come in various types, each with different strengths in determining relationships between variables. Some are highly rigorous and conclusive, while others are more exploratory. Below are the main types, ranked from least to most conclusive.

1. Case Studies & Reports

Case studies focus on individual cases or small groups, often used in medical and psychological research. They provide detailed insights but lack broader applicability due to their small sample size.

  • Involves in-depth analysis of a single subject or a few cases.
  • Often used for rare conditions or unique situations.
  • Generates hypotheses but cannot establish causation.

2. Descriptive Studies

These studies aim to describe characteristics, behaviors, or trends in a population. They do not manipulate variables and are mainly used for observational insights.

  • Survey Studies: Use questionnaires or interviews to collect data.
  • Epidemiological Studies: Track disease patterns in populations.
  • Useful for hypothesis generation but not for proving causation.

3. Observational Studies

Observational studies track participants without intervention to explore relationships between exposures and outcomes.

  • Cross-Sectional Studies: Examine data at a single point in time.
  • Case-Control Studies: Compare people with a condition to those without.
  • Cohort Studies: Follow a group over time to observe changes.
  • Mendelian Randomization: Uses genetic variants as instrumental variables to infer causation, reducing confounding and reverse causation issues found in traditional observational studies.
  • More reliable than descriptive studies but still prone to confounding factors.

4. Quasi-Experimental Studies

These studies involve an intervention but lack full randomization, making them a middle ground between observational and experimental studies.

  • Before-and-After Studies: Measure effects before and after an intervention.
  • Interrupted Time Series: Observe trends before and after a change.
  • Stronger than observational studies but weaker than true experiments due to potential bias.
  • Mechanistic Models: Utilize established principles and mathematical frameworks to explain underlying causal pathways, often used to complement empirical data.

5. Experimental Studies

Experimental studies manipulate variables in controlled conditions to establish cause-and-effect relationships.

  • Randomized Controlled Trials (RCTs): Participants are randomly assigned to experimental or control groups.
  • Double-Blind Studies: Neither participants nor researchers know who receives the treatment, reducing bias.
  • Crossover Studies: Participants serve as their own control by receiving both treatment and placebo at different times.
  • Considered the gold standard for proving causation in single studies.

6. Systematic Reviews & Meta-Analyses

These studies synthesize multiple research findings to provide the highest level of scientific evidence.

  • Systematic Reviews: Critically evaluate and summarize multiple studies on a topic.
  • Meta-Analyses: Combine statistical results from various studies to identify trends.
  • The most conclusive form of scientific evidence, reducing individual study biases.

Each study type plays a crucial role in research, from hypothesis generation to definitive proof of causation.

Another dimension to consider is that animal RCTs may be very conclusive on their own but do not necessarily apply to humans.

However if the RCTs are conclusive on very different species likes e-g: worms and apes, far apart in terms of evolution, then the conclusions could be considered to have a higher chance of also applying to humans.