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Mathematical Requirements for Research Methods

Probability, significance levels and principles of sampling

  • 5 things to know
  • Component 01

Overview

Recall questions on probability and levels of significance, p ≤ 0.05 and 0.01, Type 1 and Type 2 errors, why hypotheses are never proved, and calculating and describing random and stratified samples and why samples can be unrepresentative.

What you need to know

Answer each one before you reveal it.

Your recallNot started
Key term

Why is even a properly random sample sometimes unrepresentative?

Evaluation

Explain the difference between the 0.05 and the 0.01 levels of significance.

Key term

Why is a sample used rather than the whole population?

Key term

What does probability express in psychological research?

Key term

What effect does an unrepresentative sample have on the conclusions of a study?

Questions from this subtopic

Each one with the model answer underneath.

  1. 1

    What does probability express in psychological research?

    Model answer

    The likelihood that a result could have occurred by chance alone, given on a scale from 0 to 1.

  2. 2

    What is a level of significance?

    Model answer

    The cut-off probability a researcher sets in advance for deciding whether a result is unlikely enough to have arisen by chance to reject the null hypothesis.

  3. 3

    What does p ≤ 0.05 mean?

    Model answer

    There is a 5% or smaller probability that the result occurred by chance.

  4. 4

    Explain the difference between the 0.05 and the 0.01 levels of significance.

    Model answer

    At 0.05 there is up to a 5% probability the result is due to chance, while 0.01 is stricter and allows only up to a 1% probability.

  5. 5

    When would a researcher choose the 0.01 level rather than 0.05?

    Model answer

    When being wrong would be costly or harmful, for example in medical or drug research, or when checking a claim that has already been made.

  6. 6

    Why is the 0.05 level the usual compromise in psychology?

    Model answer

    It is strict enough to reject most chance results but lenient enough that genuine effects are not routinely missed.

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