 # Question: Under What Circumstances Can A Very Small Treatment Effect Still Be Significant?

## Which factor will increase the chances of rejecting the null hypothesis?

When we increase the sample size, decrease the standard error, or increase the difference between the sample statistic and hypothesized parameter, the p value decreases, thus making it more likely that we reject the null hypothesis..

## What is the relationship between the alpha level the size of the critical region and the risk of a type I error?

What is the relationship between the alpha level, the size of the critical region, and the risk of a Type I error? As the alpha level increases the size of the critical region increases and the risk of a Type I error increases.

## What is the typical level of significance for a hypothesis test in behavioral research?

In behavioral science, the criterion or level of significance is typically set at 5%. When the probability of obtaining a sample mean is less than 5% if the null hypothesis were true, then we reject the value stated in the null hypothesis. A decision made in hypothesis testing centers on the null hypothesis.

## Why do we use 0.05 level of significance?

The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference.

## Which of the following is an effect of increasing sample size?

Increasing the sample size decreases the width of confidence intervals, because it decreases the standard error.

## What is a likely outcome for a hypothesis test if a treatment has a very small effect?

If a treatment has a very small effect, what is a likely outcome for a hypothesis test evaluating the treatment? You complete a hypothesis test using a = . 05, and based on the evidence from the sample, your decision is to reject the null hypothesis.

## What can a researcher do to influence the size of the standard error?

What can a researcher do to influence the size of the standard error? … A standard error can be decreased by increasing the sample size. Changes in sample size have no effect on the probability of a Type I error. B.

## What happens when level of significance α is reduced?

Factors That Affect Power Significance level (α). The lower the significance level, the lower the power of the test. If you reduce the significance level (e.g., from 0.05 to 0.01), the region of acceptance gets bigger. As a result, you are less likely to reject the null hypothesis.

## What happens when the alpha level is changed from .05 to 01?

What happens to the probability of a Type 1 error when the alpha level is changed from . 05 to . 01? The probability decreases.

## How is the size of the critical region determined?

This is usually done by calculating a statistic (e.g. the sample mean) based on the observed data and determining whether the statistic is contained within the critical region. … The size of the critical region is also referred to as the size of the test (which is often denoted by α; see Level of a Test).

## What is the effect of decreasing the alpha level for example from a .05 to a 01 )?

Choice of alpha level With an alpha level of 0.01, there will be only a 1% chance of rejecting a true Ho. The change in alpha will also effect the Type II error, in the opposite direction. Decreasing alpha from 0.05 to 0.01 increases the chance of a Type II error (makes it harder to reject the null hypothesis).

## What is the effect of increasing the alpha level?

Higher values of α make it easier to reject the null hypothesis, so choosing higher values for α can reduce the probability of a Type II error. The consequence here is that if the null hypothesis is true, increasing α makes it more likely that we commit a Type I error (rejecting a true null hypothesis).

## When you increase your alpha level Which of the following is true?

Increasing the alpha level increases your chance of rejecting the null, but it also increases the chance of Type I error. If the population mean score is 80 and your hypothesis is that the treatment will INCREASE the score, then a sample score equal or less than 80 would be part of the null.

## What is the consequence of a type 1 error?

Consequences of a type 1 Error Consequently, a type 1 error will bring in a false positive. This means that you will wrongfully assume that your hypothesis testing has worked even though it hasn’t. In real life situations, this could potentially mean losing possible sales due to a faulty assumption caused by the test.

## What is the consequence of a Type II error quizlet?

In typical research situation, a type II error means that the hypothesis test has failed to detect a real treatment effect. The concern is that the research data does not show the result the researcher hoped to obtain.