What is meant by 'statistical power' and what is the ...



What is meant by "statistical power" and what is the advantage if our research has lots of it? Describe how power analyses are conducted and how they can inform our statistical decisions.

1. What is meant by "statistical power" is how willing we are to make a Type II error. The greater the power in a research study the less likely it is to have a Type II error. Power analyses are performed by obtaining the r value and then checking it on a power table along with the desired amount of power. Where the two variables intersect, gives us the amount of people needed in the study to maintain significance of our statistical descisions. The standard is 80 percent power, leaving you with a 20 percent chance of error, but you can always increase your power reducing the possibility of a Type II error.

2. Statistical power is the strength/magnitude of the study. The more power the less likely the chances of making a Type II error. There are 2 different types of power analyses. a priori power analysis and post hoc power analysis. a priori power analysis is done before the study is conducted and post hoc power analysis tells the probability of making a type II error in the study.

3. Statisical power is the ablility to be able to reject the H0:. The advantage of statistical power is that we can avoid errore. Power analyses are conducted in two ways: Apriori, which is done before the study (an estimate), and Post hoc, which is done after the study.

4. Statistical power is the sensitivity. It is the ability to determine the relationship between the variables based on sample data is the same as in the population. If our research has lots of power then this reduces the chance of committing a type II error. An a priori power analysis can be preformed in order to determine the sample size needed to find a correlation with a specified size. You begin with the effect size estimate. You use this and the power needed to find the correct sample size on the power table. A post hoc power analysis is conducted after the null hypothesis has been retained. It is used to determine the probability that you committed a type II error. You use the effect size and the sample size from the test. When looking at the power table you can identify the power. The power subtracted from 1 is the probability that a type II error occurred.

5. Statistical power is the ability to correctly retain the null. The more statistical power, the less likely there is to be a type II error, or a miss. There are two types of power analyses: a priori and post hoc. An a priori power analysis is done before the data is collected. In order to perform this, the researcher predicts the effect size (r) and decides how much risk he/she is willing to take of performing a type II error. The a priori power analysis will then tell him/her how large the sample size needs to be in order to obtain the desired power. The post hoc power analysis, on the other hand, is done after the data has been collected and the null retained. This analysis uses the obtained r and sample size to determine the probability of having a type II error.

6. Statistical power mean the sensitivity. Power is the probability that we are not making a type II error. The more power you have the less likely the dreaded type II error is. If you run a study and have only .1 power there is a .9 chance that you are making a type II error. If a study ahas lots of power they can be confident that their conclusion is accuate and that they did not make they type II error. A priori analysis is conducted before the study is run, a researcher can say how much power they want and what an approximate effect size could be and there for know how many participants they need in their sample size to get a significant effect

7. Statistical power is what makes us search for the right effect size that would show how things relate to the population. The effect size will is supposed to be the ammount of data that we collect to show how significant our data set is.

8. Statistical power is when the values calculated from the data are statistically significant and can be considered "true". You want your research to have a lot of it because that would mean your results can be interpreted as true or not true.

9. Statistical power refers to the ability to reject the null hypothesis. If an experiment has plenty of power but the H0 was retained, then there is little chance that a type II error was made. a priori analyses take effect size and a chosen power into account in order to determine the sample size for an experiment. Post hoc analyses take effect size and sample size into account to determine the power of the experiment.

10. Statistical power is related to the likelihood that we made a type II error. If we have higher power, the standard is 80%, then we only have a 20% probability of committing a type II error. We want higher power so that we do not have a miss. We can perform an a priori before we conduct the research and this tells us how many participants we should have in order to have enough power for our study. We must first estimate the amount of correlation (r) we think our study should have (small (.10), medium (.30), or large (.50)). This can be found from previous studies that are similar to this, or it can be an estimate. Then we decide how much power we want our study to have. Usually this is 80% so that we do not commit a type II error. We follow the chart and it tells us how many participants we should have in our sample. We can also run a post hoc analysis to see if we have enough power. This is done by finding our r value, or our correlation, and also our sample size. Follow the chart down to find how much power you have. In doing this, you can see if you need to add more participants to your sample to have enough power to convince yourself that you have not committed a type II error.

11. Statistical power is the power you have to reject the null hypothesis. It is an advantage to have lots of power in a study because it decreases the probability that you made a type II error. Power analyses can be done two different ways. Before you begin the study you can decide how much of a risk you are willing to take on making a type II error and find how many subjects you will need in your study to not have a type II error via an a priori power analysis. After a study is complete and you maintained the null, you can conduct a post hoc power analysis to find out the probability that you made a type II error, and how many subjects you will need to conduct a replication of your study in order to not make (possibly another) type II error.

12. Effect size estimates tell us how significant the relationship is. Looking at the value of "r" gives us this information. The size of r tells us the strength of the relationship, and the sign of r tells us the direction of the relationship. For significance tests we look at the "p" value. If p < .05 we reject the null, if p > .05 we retain the null. By putting this information together we are able to decide if the the results are significant or not, and if they are, exactly how significant they are and in what direction

13. Statistical power is ability to say we have either correctly retained or rejected the null. If we have a lot of power, we are more likely to make a correct retainment or rejection of the null. Power analyses are conducted either a priori or post hoc. A priori power anaylsis is done before the experiment. The estimated r value and the desired power are determined, and the appropriate number of participants is calculated. Post hoc power anaylsis is done after, normally when there is a type II error. This is when the study has already been run, but there are looking to increase their power. In order to do this, they must rerun their experiment with the appropriate amount of people.

14. Statistical power allows us to determine the likelyhood of making a Type 2 error. If the research has a lot of statistical power, the less likely that our results are due to a Type 2 error or miss.

Power analyses are conducted by using the effect size or r and the p-value to determine the amount of power our research has. Power analyses can inform our statistical decisions by letting us know if we need to use a larger sample, and if the results that we have obtained are significant.

15. Statistical power is an ability to reject HO: WE can reject the HO: just in case if statistical significance is lower than .05 If statistical significance is higer that .05 than we have to retaine the HO: The higher the statistical power, the lower chance of type II error whis is also called miss error. The type II error takes place when we mistakenly conclude that the variables are not related in the target population. In case of retaining HO:, we have to calculate type II error.

16. "Statistical power" is also called "sensitivity". The main a advantage is that having lots of power reduces the chances of having a Type II error. There are two ways power analysis are conducted: 1) A Priori: condcted before the study 2) Post Hoc: conducted after the study

17. Statistic power is Statistical power is the ability to avoid a type II error. You can do this in two ways with apriori power or post hoc power. If both you need to know the r. A priori is do done before the study is conducted and you either need to know the required power or type II error to find size of the sample needed for the study. A post hoc is done after the study is completed when the the null is retained. In this instance you need to know the size of the sample and the correlation to see if you have enough power for the study.

18. Statistical power is the chance that we will not miss a signifigant effect. If our research has lots of power we are more likely to find a signifigant effect and less likely to have a type II error. A power analysis is conducted by comparing the effect size and signifigance recieved to the sample size. If the power is to low then we need to increase the sample size. An a priori power analysis will tell us how large our sample needs to be in relation to the estimated effect size inorder to obtain a signifigant result. A post hock power analysis will tell us how large our sample will need to be for further research in order to obtain a signifigant effect of the size indicated by our current study.

19. Statistical power is how valid the research is. It means that if you have a lot of statistical power, then there is very little chance that the results were wrong. To do this the effect size needs to be taken into consideration, along with the number of participants to find out what chance the results have of being wrong. If we want to have and 80% chance of being right, a lot of power behind the research, then we need to look at the correlation, the r value of the first study and see how many participants are needed. If the number is higher than what was used, then the study needs to be run again with the correct number of participants to see if we can get the same effect with very little chance at being wrong, lots of power behind it.

20. Statistical power is the ability to reject the null, which means that you found an effect in your data that actually happens in the population. Using r, sample size, and power you can correctly reject the null. The advantge of having lots of power is that you reduce the chance of a type II error. More power, less type II error. Power analyses are conducted in two ways. First, an a priori can be used before the experiment to determine the sample size. Using power and r, sample size can be found to reduce type II errors. Also, post hoc can be used after the retention of the null. This is used when you want to know the probability that you have committed a type II error.

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