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3.3: Putting it all together- Inferential statistics and hypothesis testing

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    What is a hypothesis and are there different kinds?

    Biological (Scientific) hypothesis: An idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. This is the backbone of all scientific inquiry! As such it is important to have a solid biological hypothesis before moving forward in the scientific method (i.e. procedures, results, discussion). After the creation of a solid biological hypothesis, it can then be simplified into a statistical hypothesis (as defined below) that will become the basis for how the data will be analyzed and interpreted.

    Statistical hypotheses: After defining a strong biological hypothesis, a statistical hypothesis can be created based on what you will predict will be the measured outcome(s) (dependent variable(s)). If a study has multiple measured outcomes there can be multiple statistical hypotheses. Each statistical hypothesis will have two components (Null and Alternative).

    • Null hypothesis (Ho) –This hypothesis states that there is no relationship (or no pattern) between the independent and dependent variables.
    • Alternative hypothesis (H1) – This hypothesis states that there is a relationship (or is a pattern) between the independent and dependent variables.

     

    Independent versus dependent variables: For both biological and statistical hypotheses there should be two basic variables defined:

    • Independent (explanatory) variable – It is usually what phenomena you think will affect the measure you are interested in (dependent variable).
    • Dependent (response) variable – A dependent variable is what you measure in the experiment and what is affected during the experiment. The dependent variable responds to (depends on) the independent variable. In a scientific experiment, you cannot have a dependent variable without an independent variable.

     

    Example

    Yellow-billed Cuckoo nests were counted during breeding season in degraded, restored, and intact riparian habitats to see overall habitat preference for nesting sites increased with habitat health. 

    • Scientific hypothesis: Yellow-billed Cuckoo will have habitat preferences because of habitat health/status.
    • Statistical hypotheses: (Ho) There will be no differences in number of nests between habitats with different health/status. (H1) There will be more nests in restored and intact habitats compared to degraded.
    • Independent variable = Habitat health/status
    • Dependent variable = Number of nests counted

     

    How do you make conclusions?

    Finally, after defining the biological hypothesis, statistical hypothesis, and collecting all your data, a researcher can begin statistical analysis. A statistical test will mathematically “test” your data against the statistical hypothesis. The type of statistical test that is used depends on the type and quantity of variables in the study, as well as the question the researcher wants to ask. After computing the statistical test, the outcome will indicate which statistical hypothesis is more likely. This, in turn indicates to scientists what level of inference can be gained from the data compared to the biological hypothesis (the focus point of the study). Then a conclusion can be made based on the sample about the entire population. It is important to note that the process does not stop here. Scientists will want to continue to test this conclusion until a clear pattern emerges (or not) or to investigate similar but different questions.

     

    Attribution 

    Rachel Schleiger (CC-BY-NC)

    This page titled 3.3: Putting it all together- Inferential statistics and hypothesis testing is shared under a CC BY-NC 4.0 license and was authored, remixed, and/or curated by Melissa Ha and Rachel Schleiger (ASCCC Open Educational Resources Initiative) .