How can confidence intervals help us in SPSS research?

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    Confidence Interval is the range of values of a variable of interest computed to guide SPSS researchers using the sample to know where the true effect lies of the population. This would mean that if another sample is taken and the same analysis carried out, then the effects should lie within the C.I limits. The end values of the range are called the confidence interval limits i.e. Upper and lower limits.

    Confidence interval is more preferred by SPSS researchers than the p-value. The confidence interval shows the range in which the true values may lie of the population but not the actual value of the variable, hence if a value of the variable is computed and the value lies within the upper limit and the lower limit then we can conclude that the effect is statistically non significant. Otherwise if the computed value of the variable does not lies within the limits then we can conclude it’s statistically significant.

    Large samples tend to have small range of C.I while small samples have a wide range. If the C.I has a small range then the SPSS researchers are confident that effects far from this range have been ruled out making the estimates of the real effect precise. Wide range of the confidence interval on the other hand provides imprecise estimates of the real effect

    P-value could make SPSS researchers to conclude statistically significant whereas it’s not true making Type I error (rejecting null hypothesis when it’s true). For instant at 0.05 level of confidence the P-value could be less than 0.05 suggesting statistically significant which could be by chance (one out of 20), yet looking at the confidence interval the true effects lies within the limits. Also Type II error (accepting null hypothesis is false) can occur.

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