Easy & Quickest way to Learn "Hypothesis Testing"

Learn one-sample hypothesis testing with practical examples. Use z-tests with known population standard deviation and large samples, or t-tests when sigma is unknown and samples are small.

Perform a one-sample z test for a two-tailed hypothesis (mu=3) with n=100, sigma=0.8, xbar=2.84; z = -2.0 exceeds ±1.96, so reject the null.

Apply a one-sample z test for a right-tailed hypothesis with mu0=40000, sigma=3000, n=64, and sample mean 41200; reject h0 and conclude the new tires significantly improve mean life.

Apply a one-sample z test for a left-tailed hypothesis at 5% significance to decide if the mean age is below 32 (n=100, x̄=30, σ=5), and assess the agent's claim.

Learn the p-value approach to hypothesis testing by calculating the z statistic, deriving the p-value, and deciding to reject or accept the null hypothesis based on alpha; illustrated with example.

This lecture uses the interval approach for a two-tailed test with known sigma, showing how the interval 2.62 to 2.996 excludes mu equals 3 and rejects the null.

Perform a one-sample t test for a two-tailed hypothesis with n=16, sample mean 460, s=40; compute t = -4 and reject the null, concluding the mean differs from 500 g.

Use a one-sample, one-tailed t-test to test whether training works: mu0=100, n=25, x̄=130, s=15 at 5%, t=10 leads to reject the null and confirm the training raises sales.

Explore the independent samples mean test, defining null and alternative hypotheses, computing pooled variance, and deciding if there is a difference in means, illustrated with dividend yields.

Explore hypothesis testing for related samples, using before-and-after data to detect significant changes, and learn to compute paired differences, standard deviation, and t-values.

Learn how to test population proportions with a two-sample proportion test, state null and alternative hypotheses, compute p-hat and z, and draw conclusions at alpha 0.05.

Compare population variances using a two-sample f-test, state null and alternative hypotheses, compute f and critical values, and decide if dividend yields differ between NYSE and NASDAQ.

Learn how analysis of variance (anova) compares means across three or more independent groups, test null vs alternative hypotheses, with examples from schools and teaching methods.

Presents a solved anova example, comparing three groups, computing group means, grand mean, SSA and SSW, deriving the f value, and concluding there is a difference in means.

This solved example shows anova to compare three teaching methods, computing mean squares among and within groups and the f statistic, and concluding no significant difference among the methods.

Explore chi square tests for categorical data, including goodness of fit and independence, to analyze distributions, test null hypotheses, and assess associations between variables.

State the null and alternative hypotheses, compute the chi-square statistic from observed and expected frequencies, and conclude the data do not follow a uniform distribution.

Apply a chi-square test by formulating null and alternative hypotheses, calculating observed and expected frequencies, and computing the chi-square statistic, then compare with the critical value to reject the null.

Learn to test independence between fertilizer use and farm ownership with a chi-square test, form null and alternative hypotheses, compute observed and expected frequencies, and decide by the critical value.

Learn to perform a two-sample independent t-test in Excel using the Data Analysis Toolpak, assume equal variances, test hypotheses at alpha 0.05, and interpret results via critical values and p-values.

Perform a paired sample test in Excel to compare before and after remedial classes. Define null and alternative hypotheses and interpret the p-value to determine if a significant difference exists.

Learn to perform a one-way ANOVA in Excel to test differences among three players’ scores, including null and alternative hypotheses, F value versus F critical, and p-value interpretation.

Analyze a one-sample z test in Excel to determine if the population mean differs from 223, using sigma=5 and n=23, and decide whether to reject the null hypothesis.

Perform a one-sample t-test in Excel to assess if the population mean exceeds 20, using a one-tailed test with alpha 0.05, interpreting the t-statistic and p-value to reach a conclusion.