The larger the sample size the more confident you can be that the data you have collected is representative of what would happen on a larger scale. So if your results seem to prove your hypothesis right then the larger you sample size the more confident you can be in accepting your hypothesis.
I suspect you mean the scientific method. Briefly outlined, the scientific method consists of the following tasks: Specify a testable null and an alternative hypothesis based on preliminary tests and observations. Design tests that will determine the validity of the null hypothesis to a specified degree of confidence. Collect data and observations for the tests according to requirements of tests and data sample spaces. Test the hypothesis using collected data and observations. Make conclusions and report on the validity (or not) of the hypothesis. Make recommendation for further studies or applications of the experimental results.
it has no effect. density of a substance is the same no matter the size or shape of the sample.
A hypothesis must be subjected to rigorous testing before it becomes a theory. A hypothesis is used to explain some phenomenon about the natural world. Once a hypothesis has been created, it can be used to formulate predictions. These predictions in turn are then tested to be accurate through experimentation or observation.
Infrared spectroscopy cannot be used quantitatively. The sample preparation is also complex. It may be robust as the sample preparation may affect its results.
When the null hypothesis is true, the expected value for the t statistic is 0. This is because the t statistic is calculated as the difference between the sample mean and the hypothesized population mean, divided by the standard error, and when the null hypothesis is true, these values should be equal, resulting in a t statistic of 0.
You accept an alternative hypothesis when the p-value is greater than the sample a for a confidence level of 95%. The null hypothesis cannot be accepted.
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Increasing the sample size decreases the width of the confidence interval. This occurs because a larger sample provides more information about the population, leading to a more accurate estimate of the parameter. As the sample size increases, the standard error decreases, which results in a narrower interval around the sample estimate. Consequently, the confidence interval becomes more precise.
In Statistics, for making inferences or judgement from the data collected, hypothesis is laid, which is nothing but a statement made based on the sample, about the population of interest. After the experiment is complete, the values are compared with the table values, and inferences are made either by disproving the hypothesis, by accepting the null hypothesis and vice- versa It means to perform an experiment on your hypothesis (theory) to see if it is correct or not.
alternitive hypothesis
confidence
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Sample size determination is the act of choosing the number of observations or replicates to include in a statistical sample. The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample.For Confidence level c, and the critical value of Zc is the number such that the area under the statndard normal curve between -Zc and Zc equals C.n > (zcσ/E)2
The standard deviation of the sample mean is called the standard error. It quantifies the variability of sample means around the population mean and is calculated by dividing the standard deviation of the population by the square root of the sample size. The standard error is crucial in inferential statistics for constructing confidence intervals and conducting hypothesis tests.
The confidence interval becomes smaller.
The width of the confidence interval willdecrease if you decrease the confidence level,increase if you decrease the sample sizeincrease if you decrease the margin of error.
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