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If the decision in the hypothesis test of the population correlation coefficient is to reject the null hypothesis. What can you conclude about the correlation in the population?
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What if a correlation coefficient of .721 was observed in a study examined relationship between parental sense of self efficacy and level of involvement in school activities what would you conclude?
You could conclude that there is moderate correlation between parental sense of self efficacy and level of involvement in school activities. There is moderate evidence that they change in the same direction (both increase or both decrease) but you may not conclude that one causes, or is caused by, the other.Both variables change
Why do historians need to distinguish between causation and correlation?
Correlation by itself is understood not to be sufficient to conclude causation. That two variables change together in a highly correlated way could mean that changes in both variables are being controlled or caused by something altogether different that has not yet come to light or that has not been considered as the cause.
What happens if a hypothesis is tested and shown to be false?
We do not make a clear separation between "proven true" and "proven false" in hypothesis testing. Hypothesis testing in statistical analysis is used to help to make conclusions based on collected data. We always have two hypothesis and must chose between them. The first step is to decide on the null and alternative hypothesis. We also must provide an alpha value, also called a level of significance. Our null hypothesis, or status quo hypothesis is what we might conclude without any data. For example, we believe that Coke and Pepsi tastes the same. Then we do a survey, and many more people prefer Pepsi. So our alternative hypothesis is people prefer Pepsi over Coke. But our sample size is very small, so we are concerned about being wrong. From our data and level of significance, we find that we can not reject the null hypothesis, so we must conclude that Coke and Pepsi taste the same. The options in hypothesis testing are: Null hypothesis rejected, so we accept the alternative or Null hypothesis not rejected, so we accept the null hypothesis. In the taste test, we could always do a larger survey to see if the results change. Please see related links.
Express the null hypothesis H0 and the alternative hypothesis H1 in symbolic form Use the correct symbol p for the indicated parameter.For a researcher claims that 62 of voters favor gun control?
== == A random sample of 15 observations from the first population revealed a sample mean of 350 and a sample standard deviation of 12. A random sample of 17 observations from the second population revealed a sample mean of 342 and a sample standard deviation of 15. At the .10 significance level, is there a difference in the population means? ***Please show all the steps and work you used to conclude an answer and explain it as you would to a simpleton. Thank you so much. Read more: http://www.justanswer.com/questions/h0ee-null-hypothesis-h0-u1-u2alternate#ixzz0N7q0A7Ns
What graphs can you use to find the strength of correlation between two continuous data sets that isn't a scatter graph?
You cannot. Or rather, you should not. You do not know if the relationship is linear or something else. A scatter graph is the best way to establish the nature of the relationship. For example, the correlation between x and y, when y = x2 between, say, -4 and +4 is zero (because of symmetry). That would lead you to conclude that there was no relationship. You could not be more incorrect!
An individual reported a correlation of 1.25 between form A and From B of an intelligence test From this coefficient what could one conclude?
Nothing
What is example of hypothesis?
a example of a hypothesis is saying i can conclude that....
If we reject the null hypothesis what can we conclude about the alpha risk?
If we reject the null hypothesis, we conclude that the alternative hypothesis which is the alpha risk is true. The null hypothesis is used in statistics.
What if a correlation coefficient of .721 was observed in a study examined relationship between parental sense of self efficacy and level of involvement in school activities what would you conclude?
You could conclude that there is moderate correlation between parental sense of self efficacy and level of involvement in school activities. There is moderate evidence that they change in the same direction (both increase or both decrease) but you may not conclude that one causes, or is caused by, the other.Both variables change
What can you conclude if the global test of regression does not reject the null hypothesis?
You can conclude that there is not enough evidence to reject the null hypothesis. Or that your model was incorrectly specified. Consider the exact equation y = x2. A regression of y against x (for -a < x < a) will give a regression coefficient of 0. Not because there is no relationship between y and x but because the relationship is not linear: the model is wrong! Do a regression of y against x2 and you will get a perfect regression!
What is the purpose of a conclusion?
to explain why the data support or reject the hypothesis
Why do you use scientific method?
so we can ask Questions gather info guess are hypothesis test it out record owner analyze then conclude and if not try again in owner hypothesis
Why do scientific use scientific method?
so we can ask Questions gather info guess are hypothesis test it out record owner analyze then conclude and if not try again in owner hypothesis
A researcher finds that her data does not support her hypothesis what conclusion can she reach?
end the experiment and throw away the datarepeat the experiment until the hypothesis is supportedchange the hypothesisargue that the results were
Why do historians need to distinguish between causation and correlation?
Correlation by itself is understood not to be sufficient to conclude causation. That two variables change together in a highly correlated way could mean that changes in both variables are being controlled or caused by something altogether different that has not yet come to light or that has not been considered as the cause.
What are quantitative techniques?
Many of the quantitative techniques fall into two broad categories: # Interval estimation # Hypothesis tests Interval Estimates It is common in statistics to estimate a parameter from a sample of data. The value of the parameter using all of the possible data, not just the sample data, is called the population parameter or true value of the parameter. An estimate of the true parameter value is made using the sample data. This is called a point estimate or a sample estimate. For example, the most commonly used measure of location is the mean. The population, or true, mean is the sum of all the members of the given population divided by the number of members in the population. As it is typically impractical to measure every member of the population, a random sample is drawn from the population. The sample mean is calculated by summing the values in the sample and dividing by the number of values in the sample. This sample mean is then used as the point estimate of the population mean. Interval estimates expand on point estimates by incorporating the uncertainty of the point estimate. In the example for the mean above, different samples from the same population will generate different values for the sample mean. An interval estimate quantifies this uncertainty in the sample estimate by computing lower and upper values of an interval which will, with a given level of confidence (i.e., probability), contain the population parameter. Hypothesis Tests Hypothesis tests also address the uncertainty of the sample estimate. However, instead of providing an interval, a hypothesis test attempts to refute a specific claim about a population parameter based on the sample data. For example, the hypothesis might be one of the following: * the population mean is equal to 10 * the population standard deviation is equal to 5 * the means from two populations are equal * the standard deviations from 5 populations are equal To reject a hypothesis is to conclude that it is false. However, to accept a hypothesis does not mean that it is true, only that we do not have evidence to believe otherwise. Thus hypothesis tests are usually stated in terms of both a condition that is doubted (null hypothesis) and a condition that is believed (alternative hypothesis). Website--http://www.itl.nist.gov/div898/handbook/eda/section3/eda35.htmP.s "Just giving info on what you don't know" - ;) Sillypinkjade----
What happens if a hypothesis is tested and shown to be false?
We do not make a clear separation between "proven true" and "proven false" in hypothesis testing. Hypothesis testing in statistical analysis is used to help to make conclusions based on collected data. We always have two hypothesis and must chose between them. The first step is to decide on the null and alternative hypothesis. We also must provide an alpha value, also called a level of significance. Our null hypothesis, or status quo hypothesis is what we might conclude without any data. For example, we believe that Coke and Pepsi tastes the same. Then we do a survey, and many more people prefer Pepsi. So our alternative hypothesis is people prefer Pepsi over Coke. But our sample size is very small, so we are concerned about being wrong. From our data and level of significance, we find that we can not reject the null hypothesis, so we must conclude that Coke and Pepsi taste the same. The options in hypothesis testing are: Null hypothesis rejected, so we accept the alternative or Null hypothesis not rejected, so we accept the null hypothesis. In the taste test, we could always do a larger survey to see if the results change. Please see related links.
