Confidence interval describes how reliable an estimate is. We can calculate the upper and lower confidence limits of the intervals from the observed data. Suppose a dataset x1, . . . , xn is given, modeled as realization of random variables X1, . . . , Xn. Let θ be the parameter of interest, and γ a number between 0 and 1. If there exist sample statistics Ln = g(X1, . . . , Xn) and Un = h(X1, . . . , Xn) such that P(Ln < θ < Un) = γ for every value of θ, then (ln, un), where ln = g(x1, . . . , xn) and un = h(x1, . . . , xn), is called a 100γ% confidence interval for θ. The number γ is called the confidence level. In general, with a normally-distributed sample mean, Ẋ, and with a known value for the standard deviation, σ, a 100(1-α)% confidence interval for the true μ is formed by taking Ẋ ± e, with e = z1-α/2(σ/n1/2), where z1-α/2 is the 100(1-α/2)% cumulative value of the standard normal curve, and n is the number of data values in that column. For example, z1-α/2 equals 1.96 for 95% confidence.
Confidence interval describes how reliable an estimate is. We can calculate the upper and lower confidence limits of the intervals from the observed data. Suppose a dataset x1, . . . , xn is given, modeled as realization of random variables X1, . .  , Xn. Let θ be the parameter of interest, and γ a number between 0 and 1. If there exist sample statistics Ln = g(X1, . . . , Xn) and Un = h(X1, . . . , Xn) such that P(Ln < θ < Un) = γ for every value of θ, then (ln, un), where ln = g(x1, . . . , xn) and un = h(x1, . . . , xn), is called a 100γ% confidence interval for θ. The number γ is called the confidence level. In general, with a normally-distributed sample mean, Ẋ, and with a known value for the standard deviation, σ, a 100(1-α)% confidence interval for the true μ is formed by taking Ẋ ± e, with e = z1-α/2(σ/n1/2), where z1-α/2 is the 100(1-α/2)% cumulative value of the standard normal curve, and n is the number of data values in that column. For example z1-α/2 equals 1.96 for 95% confidence.