PSYC10004 Chap.10 Null-Hypothesis Significance Testing

Null-Hypothesis Significance Testing

Define null and alternative hypotheses

Null-Hypothesis Significance Testing connects structure, process and observation through null and alternative hypotheses, test statistic and p-value and decision.

The chapter is useful when the task is to state hypotheses, calculate or read the test result and separate the statistical decision from the substantive conclusion, because each claim must identify both the biological or behavioural system and the evidence used to distinguish it.

Locate null and alternative hypotheses first: name the relevant structure, population, scale or experimental condition.

An null and alternative hypotheses label is not enough; orient it relative to the neighbouring structures or comparison group that gives the label meaning.

Then use test statistic to describe the process linking starting condition to outcome.

Keep the sequence of test statistic clear, and separate an observed association from a mechanism that has actually been tested.

Use p-value and decision as the discriminating observation.

Ask what p-value and decision pattern would support the explanation, what plausible alternative could produce a similar pattern and what additional measurement would separate them.

Trace test statistic

In the application — state hypotheses, calculate or read the test result and separate the statistical decision from the substantive conclusion — move from observation to interpretation in explicit stages.

Report uncertainty around p-value and decision rather than treating a representative diagram, specimen or mean as if every case were identical.

Create an null and alternative hypotheses observation ledger: specimen, participant or system; orientation or experimental condition; feature observed; comparison; and inference.

Keep null and alternative hypotheses in the observation columns and reserve test statistic for the explanatory step. This prevents test statistic from being inferred from a diagram label or group difference without supporting evidence.

Use a contrast case to test p-value and decision.

Change one null and alternative hypotheses relation, exposure, task condition or comparison group while holding the rest of the scenario stable. Predict which p-value and decision observation should change if the proposed explanation is correct and which result would favour an alternative. That prediction gives the next measurement a clear purpose.

When revising PSYC10004, alternate identification with explanation.

First identify the relevant feature or pattern without notes; then explain how it contributes to state hypotheses, calculate or read the test result and separate the statistical decision from the substantive conclusion; finally state the uncertainty or boundary that remains.

This null and alternative hypotheses-to-test statistic sequence distinguishes recognising a familiar term from using it to answer a new scientific question.

Test with p-value and decision

A complete response should make the task visible before the detail: identify what must be decided, define the relevant terms, connect the evidence to test statistic, and use p-value and decision to test the result.

The final sentence about p-value and decision should answer the question actually asked rather than merely repeat the topic.

The controlling limit is specific: A p-value is conditional on the model and is neither the probability that the null is true nor an effect size.

Keep that p-value and decision limit beside the worked example, because it separates a careful PSYC10004 answer from one that sounds confident but claims more than the task or evidence supports.

For revision, retrieve null and alternative hypotheses, test statistic and p-value and decision without notes, explain their relationship aloud, then complete a changed version of the application: state hypotheses, calculate or read the test result and separate the statistical decision from the substantive conclusion.

Record the first failed test statistic reasoning move and repair it before attempting another case.

What this chapter covers

• 01

  null and alternative hypotheses

• 02

  test statistic

• 03

  p-value and decision

• 04

  Applying null and alternative hypotheses

• 05

  Limits of test statistic and p-value and decision

AskSia practice: apply Null-Hypothesis Significance Testing

Key terms

null and alternative hypotheses

Competing statistical claims in which the null specifies a benchmark effect and the alternative specifies a departure. Use this definition when the task is to state hypotheses, calculate or read the test result and separate the statistical decision from the substantive conclusion.

test statistic

A sample-based quantity standardised under the null model and used to assess compatibility with that model. Use this definition when the task is to state hypotheses, calculate or read the test result and separate the statistical decision from the substantive conclusion.

p-value and decision

The p-value measures extremeness under the null model; the decision compares it with a pre-specified significance level. Use this definition when the task is to state hypotheses, calculate or read the test result and separate the statistical decision from the substantive conclusion.

Null-Hypothesis Significance Testing FAQ

Exam move

Reconstruct the relationship among null and alternative hypotheses, test statistic and p-value and decision; complete the chapter application without notes; then test the result against this limit: A p-value is conditional on the model and is neither the probability that the null is true nor an effect size.

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