0
What should be included in an experiment design because of the way data is analyzed statistic?
Wiki User
∙ 6y agoWant this question answered?
Be notified when an answer is posted
Add your answer:
Earn +20 pts
What is a dependent variable in a science experiment?
A change wich occurs because of the independent variable.
Why is time plotted on the horizontal axis in an experiment?
It is because time is often, though not always, an independent variable.
Is it possible to repeat an experiment with the same number of trials in each and get a different experimental probability for each experiment?
yes because a quarter has 2 sides but flipping it you dont have a 100%chance if it lands on the same side
What is the difference between parameter and statistics?
© The statistic describes a sample, whereas a parameter describes an entire population.© Example of statistic is, if we randomly poll voters in a particular election and determine that 55% of the population plans to vote for candidate A, then you have a statistic because we only asked a sample of the population who they are voting for, then we calculated what the population was likely to do based on the sample. Alternatively the example of parameter is, if we ask a class of third graders who likes vanilla ice cream, and 90% of them raise their hands, then we have a parameter because 90% of that class likes vanilla ice cream. We know this because you asked everyone in the population.© Statistic is a random variable. But parameter is constant, it is not a random variable.
What is the difference between a parameter and a statistic?
A parameter is a number describing something about a whole population. eg population mean or mode. A statistic is something that describes a sample (eg sample mean)and is used as an estimator for a population parameter. (because samples should represent populations!)
What should be included be included in an experimental design because of the way data is analyzed?
Replication should be included in an experimental design because of the way data is analyzed using statistics.
What should be included in an experimental because of the way way data is analyzed using statistics?
Replication should be included in an experimental design because of the way data is analyzed using statistics.
What should be included in an experimental design because of the way data is analyzed using statistics?
Replication should be included in an experimental design because of the way data is analyzed using statistics.
What should be included in a experimental design because the way the data is analyzed using statistics?
Replication
What should be included in an experimental degin because of the way data is analyzed using statistics?
replication
What should be included in a experimental design because of the way data is analyzed using statistics?
Replication
What should be included in experiental design because of the way data is analyzed using statistcs?
Random replication of plots or sampling to ensure better probability results. A "control" sounds right.
Why did The Pfennigs experiment included plain brown artificial snakes?
im not sure if this is right but i think its because brown snakes were needed as a control.
Why was Benjamin Franklin's famous kite experiment so dangerous?
Because it included lightning, and lightning is very dangerous. It can cause death or severe injury.
Why is a variable used in an experiment?
Because it's an experiment not a replication...
What is the 95 percent confidence interval for statistics?
In statistics you have an experiment which will consist of one or more measurements. These measurements are converted to some statistic: it could be the sample mean, variance, maximum or something else. If you repeated the experiment, the value of this statistic would also change.If your hypothesis is true - whether in terms of the distribution or its parameters - and you repeated the experiment many times, you should expect the statistic to fall within the confidence interval (CI) in 95% of your trials. Even if the hypothesis is true, you should expect random variations to cause your statistic to lie outside the CI in 5% of cases.If you have a result that falls outside the 95% CI, it could be because you were unlucky and hit upon one of the 5% of rogue cases or that your hypothesis was incorrect. In this case you play the odds and conclude that your [null] hypothesis was incorrect.In statistics you have an experiment which will consist of one or more measurements. These measurements are converted to some statistic: it could be the sample mean, variance, maximum or something else. If you repeated the experiment, the value of this statistic would also change.If your hypothesis is true - whether in terms of the distribution or its parameters - and you repeated the experiment many times, you should expect the statistic to fall within the confidence interval (CI) in 95% of your trials. Even if the hypothesis is true, you should expect random variations to cause your statistic to lie outside the CI in 5% of cases.If you have a result that falls outside the 95% CI, it could be because you were unlucky and hit upon one of the 5% of rogue cases or that your hypothesis was incorrect. In this case you play the odds and conclude that your [null] hypothesis was incorrect.In statistics you have an experiment which will consist of one or more measurements. These measurements are converted to some statistic: it could be the sample mean, variance, maximum or something else. If you repeated the experiment, the value of this statistic would also change.If your hypothesis is true - whether in terms of the distribution or its parameters - and you repeated the experiment many times, you should expect the statistic to fall within the confidence interval (CI) in 95% of your trials. Even if the hypothesis is true, you should expect random variations to cause your statistic to lie outside the CI in 5% of cases.If you have a result that falls outside the 95% CI, it could be because you were unlucky and hit upon one of the 5% of rogue cases or that your hypothesis was incorrect. In this case you play the odds and conclude that your [null] hypothesis was incorrect.In statistics you have an experiment which will consist of one or more measurements. These measurements are converted to some statistic: it could be the sample mean, variance, maximum or something else. If you repeated the experiment, the value of this statistic would also change.If your hypothesis is true - whether in terms of the distribution or its parameters - and you repeated the experiment many times, you should expect the statistic to fall within the confidence interval (CI) in 95% of your trials. Even if the hypothesis is true, you should expect random variations to cause your statistic to lie outside the CI in 5% of cases.If you have a result that falls outside the 95% CI, it could be because you were unlucky and hit upon one of the 5% of rogue cases or that your hypothesis was incorrect. In this case you play the odds and conclude that your [null] hypothesis was incorrect.
Why would measurements effect an experiment?
Because the measurement usually requires putting the measurement device in the experiment. Just observing an experiment effects it because you are interpreting the results.
