What’s the difference between a z-score, t-score and a P value?

Hint: The z-score, t-score and a P value are all different terms related to data and statistics. First of all one should understand that both z-score and t-score are used in hypothesis testing. t-scores are generally used in place of z-scores when there is no data on the population standard deviation. We shall now study the three terms in detail to answer our question.

Complete step-by-step solution:
A z-score tells us how far from the mean our result is. This is measured in length of standard deviations. If the z-score is positive, it means the score is above the population mean, if it is negative then, the score is below the population mean and if the z-score is zero, then it is equal to the population mean.
A t-score is used when we have a smaller sample (a sample size of approximately less than 30) to analyze and population standard deviation is unknown. Just like z-scores, these are also a type of conversion of individual scores into a standard form of data.
A P value is defined as the probability that our null hypothesis will be rejected or selected. The experimenter sets the level of significance. If the P value is greater than the level of significance, then the null hypothesis is not rejected and if it is less than the level of significance, then it is rejected.
Hence, the basic differences between a z-score, t-score and a P value have been explained.

Note: From our above understanding of the z-score, t-score and the P value, we can conclude that z-scores and t-scores measure the significant difference between the population means whereas P value just provides us with some information as to reject or not to reject a null hypothesis that we had earlier set, it does not give us the actual statistics.