# Explain The Significance Of Essay Type Test Items

In the study of statistics, a statistically significant result (or one with statistical significance) in a hypothesis test is achieved when the p-value is less than the defined significance level.The p-value is the probability of obtaining a test statistic or sample result as extreme as or more extreme than the one observed in the study whereas the significance level or alpha tells a researcher how extreme results must be in order to reject the null hypothesis.While rejecting the null hypothesis is a central goal in most scientific study, it is important to note that the rejection of the null hypothesis is not equivalent to the proof of the researcher’s alternative hypothesis.

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The needed effect is much smaller since this experiment requires much 'power'.

The sample size depends on the confidence interval and confidence level.

This is to be expected because larger the sample size, the more accurately it is expected to mirror the behavior of the whole group.

Therefore if you want to reject your null hypothesis, then you should make sure your sample size is at least equal to the sample size needed for the statistical significance chosen and expected effects.We use cookies to make interactions with our website easy and meaningful, to better understand the use of our services, and to tailor advertising.For further information, including about cookie settings, please read our Cookie Policy .When you have a higher sample size, the likelihood of encountering Type-I and Type-II errors occurring reduces, at least if other parts of your study is carefully constructed and problems avoided.Higher sample size allows the researcher to increase the significance level of the findings, since the confidence of the result are likely to increase with a higher sample size.Many effects have been missed due to the lack of planning a study and thus having a too low sample size.Also, there is nothing wrong with having a too big sample size, but often much money and efforts are required to increase the sample size, and it could prove to be unnecessary.In a study that involves drawing a random sample from a larger population in an effort to prove some result that can be applied to the population as a whole, there is the constant potential for the study data to be a result of sampling error or simple coincidence or chance.By determining a significance level and testing the p-value against it, a researcher can confidently uphold or reject the null hypothesis.It is useful to do this before running the experiment - sometimes you may find that you need a much bigger sample size to get a significant result, than it is feasible to obtain (thus making you rethink before going through the whole procedure).Different experiments invariably have different sample sizes and significance levels.