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Author Question: Which of the following statements would lend support to the hypothesis that increasing scientific ... (Read 56 times)

mspears3

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Which of the following statements would lend support to the hypothesis that increasing scientific knowledge allows humans to grasp multicausality, when single causation was previously assumed to be the case in the time of the essentialists?
 
  ? (Select all that apply.)
  a. Adolescents and young adults with entitlement issues may have been raised with deprivation, not indulgence.
  b. Scientific knowledge is increasing, as the gene is mapped.
  c. The weather cannot be controlled, but it can be affected by geologic events such as volcanic eruption.
  d. Gender of the fetus, although determined by the father, may be more likely to be either male or female, depending on the timing of sexual relations.
  e. Genetic migrations can be tracked fairly effectively, by calculating the relative frequency of various Landsteiner blood types in different countries.

Question 2

Threats to statistical conclusion validity include which of the following? (Select all that apply.)
 
  a. Fishing
  b. Low statistical power
  c. Maturation
  d. Violated assumptions of statistical tests
  e. History threat



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samiel-sayed

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Answer to Question 1

ANS: A, C, E
A philosophical group known as essentialists proposed that two concepts must be considered in determining causality: necessary and sufficient. The proposed cause must be necessary for the effect to occur. (The effect cannot occur unless the cause first occurs.) The proposed cause must also be sufficient (requiring no other factors) for the effect to occur. This leaves no room for a variable that may sometimes, but not always, serves as the cause of an effect. Spare the rod and spoil the child. Everybody talks about the weather, but nobody does anything about it. The father effectively decides the gender of the child by passing on either an X or a Y to the offspring.

Answer to Question 2

ANS: A, B, D
Statistical conclusion validity is concerned with whether the conclusions about relationships or differences drawn from statistical analysis are an accurate reflection of the real world. Low statistical power increases the probability of concluding that there is no significant difference between samples when actually there is a difference (type II error, failing to reject a false null) (see Chapter 8 for discussion of the null hypothesis). Most statistical tests have assumptions about the data collected, which include the following: (1) the data are at least at the interval level, (2) the sample was randomly obtained, and (3) the distribution of scores was normal. If these assumptions are violated, the statistical analysis may provide inaccurate results. The risk of type I error increases when the researcher conducts multiple statistical analyses of relationships or differences; this procedure is referred to as fishing.





 

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