Decresing the sample size, while holding the confidence level and the variance the same, will do what to the length of wour confidence interval?

Decresing the sample size, while holding the confidence level and the variance the same, will do what to the length of wour confidence interval?
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Confidence Interval :
A confidence interval is an estimate of the population proportion, e.g, population mean, and proportion. It provides lower and upper values where the population parameter is likely to be contained. The sample size should be appropriate for the best estimate of the population parameter.
Factors that Affect Confidence Intervals :
There are three factors that determine the size of the confidence interval for a given confidence level:
Sample size
Percentage
Population size
Sample Size :
The larger your sample size, the more sure you can be that their answers truly reflect the population. This indicates that for a given confidence level, the larger your sample size, the smaller your confidence interval. However, the relationship is not linear (i.e., doubling the sample size does not halve the confidence interval).
Increasing the sample size decreases the width of confidence intervals, because it decreases the standard error.
Explanation :
Sample Size: Smaller sample sizes generate wider intervals. There is an inverse square root relationship between confidence intervals and sample sizes. If you want to cut your margin of error in half, you need to approximately quadruple your sample size.