Difference between revisions of "AP Statistics Curriculum 2007 Hypothesis Basics"
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=== Fundamentals of Hypothesis Testing=== | === Fundamentals of Hypothesis Testing=== | ||
| − | + | A (statistical) '''hypothesis test''' is a method of making statistical decisions about populations or processes based on experimental data. Null-hypothesis testing just answers the question of "how well the findings fit the possibility that chance alone might be responsible for the observed discrepancy between the theoretical model and the empirical observations". This is accomplished by asking and answering a hypothetical question. One use is deciding whether experimental results contain enough information to cast doubt on conventional wisdom. | |
| + | |||
| + | * Example: Consider determining whether a suitcase contains some radioactive material. Placed under a [http://en.wikipedia.org/wiki/Geiger_counter Geiger counter], the suitecase produces 10 clicks (counts) per minute. The '''null hypothesis''' is that there is no radioactive material in the suitcase and that all measured counts are due to ambient radioactivity typical of the surrounding air and harmless objects in a suitcase. We can then calculate how likely it is that the null hypothesis produces 10 counts per minute. If it is likely, for example if the null hypothesis predicts on average 9 counts per minute, we say that the suitcase is compatible with the null hypothesis (which does not imply that there is no radioactive material, we just can't determine!); on the other hand, if the null hypothesis predicts for example 1 count per minute, then the suitcase is not compatible with the null hypothesis and there must be other factors responsible to produce the measurements. | ||
| + | |||
| + | The ''hypothesis testing'' is also known as ''null-hypothesis statistical significance testing''. The null hypothesis is a conjecture that exists solely to be disproved, rejected or falsified by the [[AP_Statistics_Curriculum_2007_Estim_L_Mean | sample-statistics used to estimate the unknown population papameters]]. Statistical significance is a possible finding of the test, that the sample is unlikely to have occurred in this proces by chance given the truth of the null hypothesis. The name of the test describes its formulation and its possible outcome. One characteristic of hypothesis testing | ||
| + | is its crisp decision: '''reject''' or '''do not reject''' (which is not the same as '''accept'''). | ||
| + | |||
<center>[[Image:AP_Statistics_Curriculum_2007_IntroVar_Dinov_061407_Fig1.png|500px]]</center> | <center>[[Image:AP_Statistics_Curriculum_2007_IntroVar_Dinov_061407_Fig1.png|500px]]</center> | ||
| − | === | + | ===Null hypothesis=== |
| − | + | A '''Null hypothesis''' is a hypothesis set up to be nullified or refuted in order to support an ''alternate (research) hypothesis''. When used, the null hypothesis is presumed true until statistical evidence in the form of a hypothesis test indicates otherwise. In science, the null hypothesis is used to test differences in treatment and control groups, and the assumption at the outset of the experiment is that no difference exists between the two groups for the variable being compared. The null hypothesis proposes something initially presumed true. It is rejected only when it becomes evidently false, that is, when the researcher has a certain degree of confidence, usually 95% to 99%, that the data do not support the null hypothesis. | |
| + | |||
| + | === An Example=== | ||
| + | If we want to compare the test scores of two random samples of men and women, a null hypothesis would be that the mean score of the male population was the same as the mean score of the female population: | ||
| + | : ''H''<SUB>0</SUB> : μ<SUB>1</SUB> = μ<SUB>2</SUB> | ||
| + | |||
| + | where: | ||
| + | : ''H''<SUB>0</SUB> = the null hypothesis | ||
| + | : μ<SUB>1</SUB> = the mean of population 1, and | ||
| + | : μ<SUB>2</SUB> = the mean of population 2. | ||
| + | |||
| + | Alternatively, the null hypothesis can postulate that the two samples are drawn from the same population, so that the variance and shape of the distributions are equal, as well as the means............ | ||
| − | + | Formulation of the null hypothesis is a vital step in testing statistical significance. Having formulated such a hypothesis, one can establish the probability of observing the obtained data or data more different from the prediction of the null hypothesis, if the null hypothesis is true. That probability is what is commonly called the "significance level" of the results. | |
| − | + | That is, in scientific experimental design, we may predict that a particular factor will produce an effect on our dependent variable — this is our alternative hypothesis. We then consider how often we would expect to observe our experimental results, or results even more extreme, if we were to take many samples from a population where there was no effect (i.e. we test against our null hypothesis). If we find that this happens rarely (up to, say, 5% of the time), we can conclude that our results support our experimental prediction — we reject our null hypothesis. | |
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===Examples=== | ===Examples=== | ||
Revision as of 16:48, 4 February 2008
Contents
General Advance-Placement (AP) Statistics Curriculum - Fundamentals of Hypothesis Testing
Fundamentals of Hypothesis Testing
A (statistical) hypothesis test is a method of making statistical decisions about populations or processes based on experimental data. Null-hypothesis testing just answers the question of "how well the findings fit the possibility that chance alone might be responsible for the observed discrepancy between the theoretical model and the empirical observations". This is accomplished by asking and answering a hypothetical question. One use is deciding whether experimental results contain enough information to cast doubt on conventional wisdom.
- Example: Consider determining whether a suitcase contains some radioactive material. Placed under a Geiger counter, the suitecase produces 10 clicks (counts) per minute. The null hypothesis is that there is no radioactive material in the suitcase and that all measured counts are due to ambient radioactivity typical of the surrounding air and harmless objects in a suitcase. We can then calculate how likely it is that the null hypothesis produces 10 counts per minute. If it is likely, for example if the null hypothesis predicts on average 9 counts per minute, we say that the suitcase is compatible with the null hypothesis (which does not imply that there is no radioactive material, we just can't determine!); on the other hand, if the null hypothesis predicts for example 1 count per minute, then the suitcase is not compatible with the null hypothesis and there must be other factors responsible to produce the measurements.
The hypothesis testing is also known as null-hypothesis statistical significance testing. The null hypothesis is a conjecture that exists solely to be disproved, rejected or falsified by the sample-statistics used to estimate the unknown population papameters. Statistical significance is a possible finding of the test, that the sample is unlikely to have occurred in this proces by chance given the truth of the null hypothesis. The name of the test describes its formulation and its possible outcome. One characteristic of hypothesis testing is its crisp decision: reject or do not reject (which is not the same as accept).
Null hypothesis
A Null hypothesis is a hypothesis set up to be nullified or refuted in order to support an alternate (research) hypothesis. When used, the null hypothesis is presumed true until statistical evidence in the form of a hypothesis test indicates otherwise. In science, the null hypothesis is used to test differences in treatment and control groups, and the assumption at the outset of the experiment is that no difference exists between the two groups for the variable being compared. The null hypothesis proposes something initially presumed true. It is rejected only when it becomes evidently false, that is, when the researcher has a certain degree of confidence, usually 95% to 99%, that the data do not support the null hypothesis.
An Example
If we want to compare the test scores of two random samples of men and women, a null hypothesis would be that the mean score of the male population was the same as the mean score of the female population:
- H0 : μ1 = μ2
where:
- H0 = the null hypothesis
- μ1 = the mean of population 1, and
- μ2 = the mean of population 2.
Alternatively, the null hypothesis can postulate that the two samples are drawn from the same population, so that the variance and shape of the distributions are equal, as well as the means............
Formulation of the null hypothesis is a vital step in testing statistical significance. Having formulated such a hypothesis, one can establish the probability of observing the obtained data or data more different from the prediction of the null hypothesis, if the null hypothesis is true. That probability is what is commonly called the "significance level" of the results.
That is, in scientific experimental design, we may predict that a particular factor will produce an effect on our dependent variable — this is our alternative hypothesis. We then consider how often we would expect to observe our experimental results, or results even more extreme, if we were to take many samples from a population where there was no effect (i.e. we test against our null hypothesis). If we find that this happens rarely (up to, say, 5% of the time), we can conclude that our results support our experimental prediction — we reject our null hypothesis.
Examples
Computer simulations and real observed data.
- TBD
Hands-on activities
Step-by-step practice problems.
- TBD
References
- TBD
- SOCR Home page: http://www.socr.ucla.edu
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