How can you determine the validity of an experiment?

When dealing with Structural equation modeling for example, it is very important to check the validity of the measurement model under the tile of confirmatory factor analysis which means for example the construct validity and this means the degree to which the research measures the construct

Determining experiment validity involves assessing if it accurately measures intended variables (internal), applies findings beyond the study (external), aligns with theoretical concepts (construct), draws accurate conclusions from statistical analysis (statistical conclusion), and appears to measure what it claims (face).

Internal and external validity must be assessed when analysing a Design of Experiments (DOE). Internal validity is achieved by controlling confounding variables, randomly assigning experimental units to treatment conditions, and replicating results. This arrangement helps establish a cause-and-effect relationship between independent and dependent variables. External validity measures how well experiment results can be applied to different settings, populations, and time periods. This can be assessed by running the experiment under different conditions and with different samples. Interpreting experimental results, confirming effect significance, and validating conclusions requires knowledge of statistical analysis methods like ANOVA.

In statistics, the reliability analysis is very important specially when we are working on Likert scale data, it refers to the extent to which scale produces consistent results ,for example a type called internal consistency reliability is used to measure the reliability of a summated scale where several items are summer to form a total score.

Using internal consistency measures like Cronbach's alfa to assess how well the items within the scale correlate with each other , so after calculating this indicator, having high Cronbach alpha as above 0.7 leads to excellent internal consistency of the measurement scale and the items are measuring the same underlying construct.

Confronting (1) the conclusions with the method, (2) the method with the assumptions and (3) the conclusion with the assumptions. This is, essentially, applying Aristotelian logic to experiments validation. Also, by trying to replicate the experiment. A valid experiment needs to have, ideally: repeatability (the researcher being able to repeat his/her own experiment results with the same method), replicability (a researcher being able to replicate the experiment results of a colleague using the same method) and reproducibility (a researcher being able to reproduce the experiment results of a colleague using a different method). The last one is a plus.