In general, the answer is no, both negative and positive z score values should be expected. A z-score (or standardize score) is the raw data value minus the mean and then this result divided by the standard deviation. If the data can be considered normally distributed and a random sample is taken from a population, then as the sample size becomes large, approximately half the z-scores should be negative and half of the z-scores should be positive. There are some exceptions. Small data sets may have only positive values. A non-normal (skewed) distribution if skewed to the right, may have, after normalizing, may have a higher portion of z scores as positives.
No it is not.
A negative z-score indicates that the observed value (or statistic) was below the mean. In non-directional tests, a negative z-score is just as likely as a positive one.
No. If the underlying distribution is approximately Normal then 1.4 is not at all unusual.
In the same way that you would convert a positive z-score. Only leave a negative sign in front of it.
The sign of the z score is negative if the observation was below the mean and positive if it was greater.
No it is not.
A negative z score is a value that is less than the mean value.
z-score of a value=(that value minus the mean)/(standard deviation). So if a value has a negative z-score, then it is below the mean.
A negative z-score indicates that the observed value (or statistic) was below the mean. In non-directional tests, a negative z-score is just as likely as a positive one.
Yes. If a score is below the mean, the z score will be negative.
no, z score can be negative but a probability is a always positive between 0 and 1.
Let z be positive so that -z is the negative z score for which you want the probability. Pr(Z < -z) = Pr(Z > z) = 1 - Pr(Z < z).
No. If the underlying distribution is approximately Normal then 1.4 is not at all unusual.
In the same way that you would convert a positive z-score. Only leave a negative sign in front of it.
The sign of the z score is negative if the observation was below the mean and positive if it was greater.
A z score of -1.3 means that the score is located at the negative 1.3 sigma level with respect to the mean.
Assume the z-score is relative to zero score. In simple terms, assume that we have 0 < z < z0, where z0 is the arbitrary value. Then, a negative z-score can be greater than a positive z-score (yes). How? Determine the probability of P(-2 < z < 0) and P(0 < z < 1). Then, by checking the z-value table, you should get: P(-2 < z < 0) ≈ 0.47725 P(0 < z < 1) ≈ 0.341345