empirical

Examples of empirical in the following topics:

• Lab: Probability Topics

  • The student will use theoretical and empirical methods to estimate probabilities.
  • Record the results in the "With Replacement" column of the empirical table.
  • Use the data from the "Empirical Results" table to calculate the empirical probability questions.
  • If you increased the number of times you picked 2 M&M's to 240 times, why would empirical probability values change?
  • Would this change (see (3) above) cause the empirical probabilities and theoretical probabilities to be closer together or farther apart?

• Lab: Continuous Distribution

  • The student will compare and contrast empirical data from a random number generator with the Uniform Distribution.
  • Construct a histogram of the empirical data.
  • Construct a histogram of the empirical data.
  • Are the empirical values (the data) in the section titled "Collect the Data" close to the corresponding theoretical values above?
  • How would that affect what you would expect the empirical data to be and the shape of its graph to look like?

• Scientific Method

  • One of the hallmarks of the scientific method is that it depends on empirical data.
  • Theories in science can never be proved since one can never be 100% certain that a new empirical finding inconsistent with the theory will never be found.
  • In general, a theory is developed by a scientist who is aware of many empirical findings on a topic of interest.
  • That is, that it is simple in the sense that it uses relatively few constructs to explain many empirical findings.

• Lab 1: Discrete Distribution (Playing Card Experiment)

  • The student will compare empirical data and a theoretical distribution to determine if everyday experiment fits a discrete distribution.
  • Knowing that data vary, describe three similarities between the graphs and distributions of the theoretical and empirical distributions.
  • Describe the three most significant differences between the graphs or distributions of the theoretical and empirical distributions.

• Lab 2: Discrete Distribution (Lucky Dice Experiment)

  • The student will compare empirical data and a theoretical distribution to determine if a Tet gambling game fits a discrete distribution.
  • Knowing that data vary, describe three similarities between the graphs and distributions of the theoretical and empirical distributions.
  • Describe the three most significant differences between the graphs or distributions of the theoretical and empirical distributions.

• Tests of Significance

  • Tests of significance are a statistical technology used for ascertaining the likelihood of empirical data, and (from there) for inferring a real effect.
  • Tests of significance are a statistical technology used for ascertaining the likelihood of empirical data, and, from there, for inferring a real effect, such as a correlation between variables or the effectiveness of a new treatment.
  • Beginning circa 1925, Sir Ronald Fisher—an English statistician, evolutionary biologist, geneticist, and eugenicist (shown in )—standardized the interpretation of statistical significance, and was the main driving force behind the popularity of tests of significance in empirical research, especially in the social and behavioral sciences.
  • Sir Ronald Fisher was an English statistician, evolutionary biologist, geneticist, and eugenicist who standardized the interpretation of statistical significance (starting around 1925), and was the main driving force behind the popularity of tests of significance in empirical research, especially in the social and behavioral sciences.

• Lab 1: Normal Distribution (Lap Times)

  • The student will compare and contrast empirical data and a theoretical distribution to determine if Terry Vogel's lap times fit a continuous distribution.
  • The empirical probability that a randomly chosen lap time is more than 130 seconds =

• Lab 2: Normal Distribution (Pinkie Length)

  • The student will compare empirical data and a theoretical distribution to determine if data from the experiment follow a continuous distribution.
  • What is the empirical probability that a randomly chosen pinkie length is more than 6.5 cm?

• Quantile-Quantile (q-q) Plots

  • The CDF based upon the sample data is called the empirical CDF (ECDF), is denoted by
  • The q-q plot for uniform data is very similar to the empirical CDF graphic, except with the axes reversed.
  • The theoretical and empirical CDFs are shown in Figure 4 and the q-q plot is shown in the left frame of Figure 5.
  • In particular, the theoretical quantile corresponding to the empirical quantile z(i) should be
  • The empirical and theoretical cumulative distribution functions of a sample of 100 uniform points

• Guidelines for Plotting Frequency Distributions

  • The relative frequency (or empirical probability) of an event refers to the absolute frequency normalized by the total number of events.