the sign test

• what is the point of statistical testing?

  only by using a statistical test can psychologists state with confidence that it was the IV that caused a change in the DV and not chance factors.
• using a statistical test allows the researcher to accept the hypothesis or reject and accept the null hypothesis
• the sign test tells us if the difference between the means is significant enough to say it was the IV that caused the change in the DV and not chance
• when a psychologist conducts a study, they must set a significance level. This is what is used to check for differences/significant relationships
• the probability is usually 0.05 (5%) for a one-tailed test
• the probability is usually 0.10 (10%) for a two-tailed test
• a 0.05 (5%) probability means that the researcher can be 95% confident that the results are due to the change in the IV and not chance.

  There is a 5% chance of making a type 1 error (the results are due to chance)
• 0.01 (1%) probability is used when the researcher needs to be more confident and eliminate as much as possible the probability of chance affecting the results
• Type 1 error

  • False positive.
  • Rejecting the null hypothesis, when there is a possibility that the results were due to chance.
  • Often caused by using a significance level that is too lenient e.g. 10%, 0.10, 1 in 10, p≤0.10. Not being cautious enough.
• Type 2 error

  • False negative.
  • Accepting the null hypothesis, when there is a possibility that the results were significant.
  • Often caused by using a significance level that is too strict e.g. 1%, 0.01, 1 in 100, p≤0.01. Being over cautious.
• Using a 0.05 significance level guards against making either a Type I or a Type II Error
• A Type I Error is likely to have occurred because the significance level of 0.10 has been set too high. Dr Stats could rectify the error by setting his probability level at 0.05 which means that the probability of chance factors affecting the result is 5% or less.