Probability and Significance

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  • Probability and Significance
    • PROBABILITY - A measure of the likelihood that an event will occur where 0 indicates statistical impossbility and 1-statistical certainty.
  • SIGNIFICANCE - how sure we are that a difference or correlation exists. Signif results mean the researcher can reject null hypothesis.
  • Null Hypothesis - researchers begin with a hypothesis (directional or non-directional). Stats test determines which hypothesis is true and if we accept or reject null hypothesis.
  • Levels of Significance and Probability - all stats test employ a significance level which is 0.05 (5%) for psychology.
  • Use of Statistical Tables
  • Once a stat test has been calculated, the result is a number - the calculated or observed value.
  • To check for statistical significance, the calculated value must be compared with a critical value
  • Critical value tells us if we can reject or accept the null hypothesis.
  • Each stats test has its own table of critical values. For some, the calculated value must be equal to or greater than the critical value. For others, the calculated value must be equal to or less than CV.
  • 3 criteria to know what CV to use.
    • Using tables of critical values
      • Level of significance - p value. 0.05.
        • One-tailed or two-tailed? - One tailed if hypothesis is directional, two tailed if hypothesis is non-directional
          • Number of participants - usually appears as the N value
  • Due to the fact that researchers can never be 100% certain that they have found statistical significance, it is possible the wrong hypothesis may be accepted.
    • Type I and Type II errors
      • Type I error - null hypothesis is rejected and alternative hypothesis is accepted when it should be other way round. FALSE POSITIVE
        • Often referred to as optimistic error as researcher claims to have found significant difference or correlation which does not exist.
          • More likely to make a Type I error if significance level is too lenient
  • Type II error is more likely if significance level is too low, as potentially significant values may be missed.
    • Type II error - Failure to reject a false null hypothesis (false negative).

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