Stat hypothesis testing

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  1. What do we use sample means for?
    To test hypotheses about population means because M approximates u
  2. What does standard error tell us?
    How well our sample mean approximates the population mean

    Or how much difference we expect between our sample mean and population mean
  3. Obtained difference between the data and hypothesis is
    The standard difference between M and u
  4. Sample variance equation

  5. Estimated standard error formula

  6. When is the t statistic used?
    When the population variance is unknown
  7. t score formula
  8. As the df for a sample increases,
    the better the sample variance represents the population variance and the better the sample mean represents the population mean
  9. As as the sample variance approaches the population variance,
    the more the t becomes like a z
  10. Sampling distribution of t characteristics 

    shape depends on?
    how does it compare to z?
    t approximates?
    • Symmetrical, unimodal
    • u=0
    • shape depends on sample size n
    • higher tails and lower peak than z
    • t approximates z distribution as n approaches infinity
  11. If prescise df is not shown use
    the critical value for the next smallest df shown
  12. p value 

    -Probability of
    -Probability of
    • -making a Type I error
    • -probability in the tail
  13. Variance (r2)

    -Define: 1, 2)
    • -the percent of the total variance accounted for by the treatment
    • -a ratio of variability: variability due to treatment effect/ total variability
    • r2=t2/t2+df
  14. What does r2 measure?

    • -How much variability is explained by the treatment
    • -How much does removing the treatment effect reduce the variability?
  15. r2 effect size

    • -0.01
    • -0.09
    • -0.25
  16. Confidence interval

    -Centered around?
    • -The treated population mean from our treated sample mean
    • -Interval or range of values centered around a sample statistic
    • -See chart
  17. T vs. z

    -Probability of extreme values is higher for
    -Critical values will be larger for
    -Critical values of t get closer to z as
    • -t
    • -t
    • -sample size increases
  18. -Too much variance is bad
    -Higher variance=
    • -Big differences between individual scores make it harder to see overall trends
    • -bigger standard error
  19. How can you reduce variance?

    • -Decrease experimental error
    • -Increase sample size to reduce standard error
Card Set
Stat hypothesis testing
Stat hypothesis testing
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