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Answers to the checkpoints of chapter 5

Checkpoint 1 

  •  When it is unbiased, it is valid and accurate; when it is accurate, it is reliable (consistent) and the distribution is narrow.
  • It affects the “validity.”
  • It affects the accuracy.
  • Invalid research can still be reliable (consistently missing the target).


Checkpoint 2

  • You can estimate the population mean, the population standard deviation and the population proportion.
  • You can estimate the mean and the proportion in a population based on the sample mean and the sample proportion.
  • For a large number of draws (large sample, repeating experiments) the distribution of a variable is approximated by the normal distribution.
  • By having a larger sample.
  • The standard deviation of the sample mean.


Checkpoint 3

  • Area within which it can be assumed with …% certainty that the actual value falls within this area.
  • With a 99% confidence level.
  • With small sample sizes (less accurate).
  • With a large standard error (less accurate).
  • The same mean: all symmetrical with the same center/curve.
    Different distribution: each shape is different, from low and wide to high and narrow. Whereas the midpoint is the same for all of them, the distribution is clearly different.


Checkpoint 4

  • A confidence interval based on the t distribution.
  • For a sample larger than 120.
  • The ∞ refers to “infinity.” That is the point from where the t and z distributions approach each other. 
  • The standard error is the spread of the sample distribution, i.e. the standard deviation divided by the square root of n.


Checkpoint 5

  • A hypothesis is an expectation about the results of variables in your population, for instance, there is or is not difference, correlation or effect.
  • This is a null hypothesis.
  • Inductive statistics involves using sample analyzes to draw conclusions about the population. Another term for this is “inferential statistics.”


Checkpoint 6

  • This is also known as the significance level.
  • If p = 0.03 in a one-tailed test, we reject the null hypothesis.
  • In a two-tailed test, p < ½ α. So 0.03 < 0.025. That is not the case, so the hypothesis is not rejected.
  • There is a difference (no direction).


Checkpoint 7

  • The extent to which you reject null hypotheses that are not true, i.e. rejecting them is justified.
  • Under hypothesis
    H0: The woman is not pregnant
    H1: The woman is pregnant
    Type II: the pregnancy test says she’s not pregnant, when in fact she is.
    Type I: the pregnancy test says she’s pregnant, when in fact she is not.
  • Reliability of a test is the chance that H0 will not be rejected because H0 is true.
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