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Statistical validity refers to the degree to which the results of an experiment are accurate and reliable. In the context of a designed experiment, statistical validity ensures that the outcomes are truly representative of the larger population, minimizing biases.
Randomization plays a key role in maintaining statistical validity. By randomly assigning experimental units to different treatments, you reduce the chances of systematic errors. This approach helps ensure that the variations observed in the outcomes can be attributed to the treatments themselves, rather than external factors.
Without statistical validity, your experiment's conclusions may not be trustworthy. It's crucial to design your study carefully to avoid biases and account for variability within the experimental conditions. Randomization is a fundamental tool in achieving this.

A random number generator (RNG) is a tool used to produce a sequence of numbers that cannot be predicted. In the context of a designed experiment, an RNG is crucial for ensuring that the assignment of experimental units to treatments is genuinely random.
There are various methods to generate random numbers:

• Software-based: Programs like Python or R can produce random numbers.
• Online tools: Websites offer random number generation services.

Using a random number generator helps in avoiding selection bias, ensuring that each experimental unit has an equal chance of being assigned to any treatment.
For instance, in an experiment with three treatments and 90 units, you could generate a list of random numbers and assign the units accordingly. The first 30 numbers could be assigned to Treatment A, the next 30 to Treatment B, and the final 30 to Treatment C.

Experimental units are the smallest division of subjects or materials to which treatments are applied in an experiment. They are the individual participants or items that receive one of the various treatments being studied.
In a carefully designed experiment, it's critical to define your experimental units clearly and ensure they are randomly assigned to treatments. This helps to reduce biases and provides a more accurate picture of the treatment effects.
Suppose you have 90 experimental units and three treatments. Each unit should be labeled uniquely from 1 to 90. Then, you'd use a random number generator to assign these units to one of the three treatments, ensuring that they are distributed evenly.
This distribution guarantees that the data collected reflect a fair and unbiased representation of the treatment effects, ensuring the accuracy and reliability of your experiment’s results.