Not looking for a lot of values between…. for true sig.
In statistics, the term “significance” refers to the degree of confidence we have in the relationship or difference observed in a sample
In statistics, the term “significance” refers to the degree of confidence we have in the relationship or difference observed in a sample. When we say “true significance,” we are referring to results that are not likely due to mere chance or random variation.
To determine true significance in a statistical analysis, we calculate a p-value. The p-value measures the probability of obtaining results as extreme as the ones observed, assuming that the null hypothesis is true. It tells us how likely it is that the observed relationship or difference occurred by chance.
When you mention “not looking for a lot of values between” in relation to true significance, it seems like you might be referring to the width of a confidence interval. In statistical inference, a confidence interval provides a range of possible values for an unknown parameter, such as a population mean. The wider the interval, the less precise our estimate is.
If you don’t want a lot of values between your confidence interval, it means you want a narrower range where the true value is more likely to be. This reflects a higher level of precision in your estimate. It indicates that you have a smaller degree of uncertainty in pinpointing the true value of the parameter you are estimating.
Therefore, when aiming for “true significance,” you typically want a small p-value, indicating that the observed relationship or difference is not likely due to chance. Additionally, you would prefer a narrower confidence interval, indicating a more precise estimate of the parameter value.
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