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Psyc_315_-_Winter_2010_-_Class_17

# Psyc_315_-_Winter_2010_-_Class_17 - Decision Errors Effect...

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Decision Errors, Effect Size and Power 2 Decision Errors • Errors that can happen during the hypothesis-testing process … Decision errors are situations in which the right procedures lead to the wrong decisions . • Decision errors occur because in hypothesis-testing we are making inferences about populations based on the information available from samples. 3 Decision Errors • The problem in hypothesis testing is deciding whether or not the sample data are consistent with the null hypothesis. “It is possible that the data obtained from a single experiment can be misleading and cause a researcher to make incorrect decisions” . • Despite our best intentions, decision errors are possible. • Ex: when we choose a significance level, we reject the null hypothesis if a sample’s mean is so extreme as to be unlikely (p < 0.05) if the null hypothesis were true. • This small probability is not a zero probability – even extreme scores happen by chance when H o is true.

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4 Decision Errors • This is what is called a TYPE I ERROR : an alarm without a fire (false alarm). • Conversely, a TYPE II ERROR is a fire without an alarm. • One way to avoid a Type I Error is to just remove the batteries! Unfortunately, this increases the incidence of Type II Errors! • Similarly, reducing the chances of Type II Error, for example by making the alarm hypersensitive (significance level from 0.05 to 0.01) can increase the number of false alarms (or Type I Error). 5 Think of the null hypothesis as the condition of no fire, while the alternative hypothesis is that a fire is burning. The alarm corresponds to rejection of the null hypothesis. NO ERROR TYPE II TYPE I NO ERROR NO FIRE FIRE NO ALARM ALARM NO ERROR TYPE II TYPE I NO ERROR H o H 1 ACCEPT H o REJECT H o TRUE STATE TRUE STATE 6 Type I Error ( α ) Rejecting a null hypothesis when it is the true state of nature. • In other words, this is the error of accepting a research hypothesis (the real hypothesis of interest) when an observation is due to chance. In English , it occurs when we are observing a difference when in truth there is none b a false alarm . • Examples: – finding an innocent person guilty – alarm going off when there is no fire. – Drug has an effect when actually it does not.
7 Type I Errors • Researchers do not know when they make this error. Due to Hypothesis Testing conventions, however, the probability of making a Type I error is kept low … What is the chance of making a Type I error? It is equal to the significance level we select to reject the null hypothesis. – So when we use a p < 0.05 significance level, there is a

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• Winter '10
• AfroditiPanagopoulos
• Null hypothesis, Statistical hypothesis testing, Statistical significance, Statistical power, Effect size, power Power

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Psyc_315_-_Winter_2010_-_Class_17 - Decision Errors Effect...

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