SCI110-TW1

Understanding of scientific inquiry begins with the exploration of life and its origins.
Historical beliefs about life creation, such as abiogenesis, led to incorrect hypotheses.
- Example: The recipe by van Helmont (1671) claimed putting a soiled shirt with wheat could create mice, later disproven by evidence.
Science relies on testing hypotheses against evidence.

Scientific disciplines evolve through research, which mandates:
- Mastery of research language, tools, and principles.
- Ability to read, understand, and critique research literature.
- Engagement in conducting research is encouraged.
Decisions in science are driven by evidence (data).

In 1988/1989, South Australia saw an increase in cases of the Legionella longbeachae bacterium, particularly among gardeners using potting mix.
The hypothesis tested was the association between potting mix usage and infection rates.
- Data was collected from 100 individuals: 25 infected and 75 age/sex-matched controls.
- Conclusion: Potting mix was a partial factor contributing to increased infections, leading to public health recommendations.

Qualitative Research:
- Focuses on feelings and opinions.
- Utilizes words, pictures, and small sample sizes; results are not generally applicable.
- Data gathered through interviews and focus groups for hypothesis generation.
Quantitative Research:
- Involves measured, observable data to test hypotheses.
- Uses numerical methods (averages, percentages); often larger sample sizes and more efficient.
- Data employs experiments and surveys for generalizable conclusions.

Combining qualitative and quantitative research offers a more holistic understanding.
Quantitative research focuses on structured numerical data:
- Data exemplifies observations and measurements from studies (numbers, text, etc.).
- Datasets are organized collections of this data.

Statistical software serves purposes like creating scientific graphs and analyzing large datasets.
Caution with spreadsheets arises from human error and complexity in locating errors.
Jamovi software will be briefly addressed in the course.

Carefully formulated RQs lead to appropriate answers, classified into four main types:
1. Descriptive RQs
2. Relational RQs
3. Repeated-measures RQs
4. Correlational RQs

Focus on populations, outcomes, and samples:
- Definition: A population includes all individuals from which observations will be derived.
- A sample is a subset of that population.
Examples of populations:
- German males aged between 18-35.
- Elderly females with glaucoma in Canada.

Inclusion criteria: characteristics required for inclusion in the study.
Exclusion criteria: characteristics that disqualify individuals from being in the study.

The outcome is a result that is numerically summarized based on the population.
Example outcomes include:
- Average weight loss after a set diet period.
- Average heart rate change post-exercise.

These RQs compare outcomes across groups within a population:
- Can either estimate or determine if outcomes are the same across comparisons.
Example RQ: Comparing heart rates between drug-dosed groups.

Focus on comparing outcomes multiple times within the same individuals:
- Example: Comparing pre-test and post-test results in individuals.

Investigates relationships between two variables:
- Example RQs: Investigate how caffeine consumption relates to heart rates.

Emphasizes establishing a clear relationship between response and explanatory variables while controlling external influences.
Essential for making credible conclusions from research findings.

Concerns generalizing study findings to the intended populations, often improved through random sampling.
Familiarity with appropriate sampling methods enhances reliability.

Understanding the differences between observational and experimental studies:
- Experimental: Researcher manipulates the variables (e.g., treatment allocation).
- Observational: Researcher observes without manipulation.
Importance of proper study design for achieving reliable and valid results.