NO.10 Logs
Example
Take PCR for example, Log2 makes sense with this data, because each time the machine goes through a cycle, the number of PCR products doubles.
The first time we do this PCR, we get a baseline for the number of transcripts for a gene. We'll set this 1. In log2 land,0 becomes our baseline.
The first time we do the PCR, the machine says there are twice as many transcripts as the first time. In other words, the difference between the first and second runs was 1 cycle in the machine.
Then third time we do the PCR, the machine says there are 8 times as many transcripts as the first time. In other words, the difference between the first and second runs was 3 cycles in the machine.
NO.33 P-Values
p-value and probability are related, but not the same.
A p-value is the probability that random chance generated the data, or something else that is equal or rarer.
So, a p-value consists of 3 parts:
1. The probability that random chance generated the data that you want to observe.
2. To add anyting else in the outcome that has equal probability.
3. To add on anything rarer.
In this example the probability of measuring someone between 155.4 and 156 cm is tiny (0.04) but the p-value is huge (1). That means there's nothing special about measuring someone who has the average height even that particular event is relatively rare.
