Mean is the average, sum total divided by total number of data entries.
Standard deviation is the square root of the sum total of the data values divided by the total number of data values.
The standard normal distribution is a distribution that closely resembles a bell curve.
95 percent of measurements are less than 2 standard deviations away from the mean, assuming a normal distribution.
For a normal probability distribution to be considered a standard normal probability distribution, it must have a mean of 0 and a standard deviation of 1. This standardization allows for the use of z-scores, which represent the number of standard deviations a data point is from the mean. Any normal distribution can be transformed into a standard normal distribution through the process of standardization.
In a standard normal distribution, approximately 95% of the data falls within two standard deviations (±2σ) of the mean (μ). This means that if you take the mean and add or subtract two times the standard deviation, you capture the vast majority of the data points. This property is a key aspect of the empirical rule, which describes how data is spread in a normal distribution.
The probability of the mean plus or minus 1.96 standard deviations is 0. The probability that a continuous distribution takes any particular value is always zero. The probability between the mean plus or minus 1.96 standard deviations is 0.95
2.275 %
95% is within 2 standard deviations of the mean.
99.7% of scores fall within -3 and plus 3 standard deviations around the mean in a normal distribution.
0.674 sd.
95 percent of measurements are less than 2 standard deviations away from the mean, assuming a normal distribution.
For a normal probability distribution to be considered a standard normal probability distribution, it must have a mean of 0 and a standard deviation of 1. This standardization allows for the use of z-scores, which represent the number of standard deviations a data point is from the mean. Any normal distribution can be transformed into a standard normal distribution through the process of standardization.
95%
about 68%
In a standard normal distribution, approximately 95% of the data falls within two standard deviations (±2σ) of the mean (μ). This means that if you take the mean and add or subtract two times the standard deviation, you capture the vast majority of the data points. This property is a key aspect of the empirical rule, which describes how data is spread in a normal distribution.
The probability of the mean plus or minus 1.96 standard deviations is 0. The probability that a continuous distribution takes any particular value is always zero. The probability between the mean plus or minus 1.96 standard deviations is 0.95
2.275 %
In a normal distribution, approximately 95% of the data falls within 2 standard deviations of the mean. This is part of the empirical rule, which states that about 68% of the data is within 1 standard deviation, and about 99.7% is within 3 standard deviations. Therefore, the range within 2 standard deviations captures a significant majority of the data points.
In a normal distribution, approximately 68% of scores fall within one standard deviation of the mean (between -1 and +1 standard deviations). About 95% of scores fall within two standard deviations (between -2 and +2 standard deviations). Therefore, the percentage of scores that falls specifically between the mean and -2 to 2 standard deviations is about 95% minus the 50% that is below the mean, resulting in approximately 45%.