The result of a random experiment provides one outcome out of all possible outcomes. Statistical methods provide solution for such experiments for precise decision making under uncertainty. The decision making under uncertainty requires three principal stages where experimenter should take adequate precautions. First precaution requires appropriate application of statistical techniques for sample selection and subsequently data collection for the required variables from the specified selected units. Second precaution requires application of appropriate methods for data analysis and presentation of the analysis for achieving the objectives. Third one requires appropriate interpretation of the data analysis. This chapter discusses all the three precautions along with suitable examples of from forestry experiments using basic statistical methods. Introductory remark about the forestry experiments along with basics of statistics, that is, variable and measurement scale is elaborated with examples. Second section discusses the data collection protocol using suitable examples from forestry field for the basic experiments, that is, sampling methods and designing of the experiments. Third section deals with data analysis for forestry experiments along with suitable examples. Data analysis includes the representation of data, that is, qualitative and quantitative method of data representation. The inferential examples are included for testing of the population(s) besides modeling of the cause and effect relationship. Each example is elaborated and interpreted for precise information of the experiment as per the data analysis.