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Title: hypothesis testing for general network models.
Abstract: The network data has attracted considerable attention in modern statistics. In research on complex network data, one key issue is finding its underlying connection structure given a network sample. The methods that have been proposed in literature usually assume that the underlying structure is a known model. In practice, however, the true model is usually unknown, and network learning procedures based on these methods may suffer from model misspecification. To handle this issue, based on the random matrix theory, we first give a spectral property of the normalized adjacency matrix under a mild condition. Further, we establish a general goodness-of-fit test procedure for the unweight and undirected network. We prove that the null distribution of the proposed statistic converges in distribution to the standard normal distribution. Theoretically, this testing procedure is suitable for nearly all popular network models, such as stochastic block models, and latent space models. Further, we apply the proposed method to the degree-corrected mixed membership model and give a sequential estimator of the number of communities. Both simulation studies and real-world data examples indicate that the proposed method works well.
Subjects: | Methodology (stat.ME) |
Cite as: | [stat.ME] |
(or [stat.ME] for this version) | |
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Do directors’ network positions affect corporate fraud.
2. theoretical analysis and hypotheses, 2.1. directors’ network positions and corporate fraud, 2.2. the mediating role of corporate internal control, 2.3. the mediating role of external auditing, 3. data and methodology, 3.1. data and sample selection, 3.2. variable measurement, 3.2.1. dependent variable, 3.2.2. independent variable, 3.2.3. mediating variable, 3.2.4. control variable, 3.3. model setting, 3.3.1. basic regression model, 3.3.2. modeling the mediating effects of internal controls, 3.3.3. modeling the mediating effect of external audit, 4. empirical analysis, 4.1. descriptive statistics, 4.2. basic regression results, 4.3. mechanism test and results, 4.4. endogeneity test, 4.5. robustness test, 5. further analysis, 6. discussion and conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.
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Type | Name | Symbol | Definitions |
---|---|---|---|
Dependent Variable | Corporate Fraud | Fraud | Fraud is a dummy variable that equals one if the firm commits fraud, and zero otherwise. |
Independent Variable | Directors’ Network Position | Centrality | The semi-local centrality of the top node. |
Mediating Variable | Internal Control | DBI | DIBO Internal Control Index |
External Audit | Big 4 | Audit by a Big 4 international firm takes the value of 1, otherwise 0. | |
Control Variable | Leverage Ratio | Lev | Total liabilities at the end of the year divided by total assets at the end of the year. |
Enterprise Value | Tobin’s Q | Ratio of the market value of an enterprise’s assets to their replacement cost. | |
Ownership Concentration | Top 1 | Shareholding of the first largest shareholder among all shareholders. | |
Return on Net Assets | ROE | The ratio of a firm’s net profit to its average net worth, reflecting the level of compensation received by owners’ equity. | |
Company Size | Size | The natural logarithm of the company’s total assets. | |
Time to Market | Age | Company listing age. | |
Board Size | Board | Number of board members. | |
Nature of Property Rights | SOE | Depending on the nature of the company’s beneficial owner, the variable takes the value of 1 for state-owned firms and 0 for non-state-owned firms. | |
Shareholding Ratio of Institutional Investors | Indsh | Institutional investor shareholding as a percentage of total equity. | |
CEO duality | Dual | The dummy variable equals 1 if the Chairman and CEO are the same person, 0 otherwise. | |
Industry | Ind | Industry dummy variables are assigned as binary indicators based on the standard industry classification, with a value of 1 for companies in a specific industry and 0 for all others. | |
Year | Year | Year dummy variables are assigned a value of 1 for the relevant. year and 0 for all other years. |
Variant | N | Mean | Std. Dev. | Median | Min | Max | VIF |
---|---|---|---|---|---|---|---|
Fraud | 43439 | 0.363 | 0.481 | 0.000 | 0.000 | 1.000 | - |
Centrality | 43439 | 3.530 | 1.254 | 3.850 | 0.000 | 5.236 | 1.13 |
Lev | 43439 | 0.455 | 1.295 | 0.455 | −0.195 | 178.345 | 1.40 |
Tobin’s Q | 43439 | 2.551 | 71.783 | 2.055 | 0.000 | 14,810.306 | 1.39 |
Top1 | 43439 | 34.162 | 15.22 | 34.162 | 0.000 | 100.000 | 1.24 |
ROE | 43439 | 0.031 | 4.405 | 0.031 | −207.397 | 713.204 | 1.00 |
Size | 43439 | 22.079 | 1.373 | 17.560 | 11.348 | 28.636 | 1.05 |
Age | 43439 | 13.93 | 8.502 | 13.000 | 0.000 | 32.000 | 1.31 |
Boardsize | 43439 | 10.446 | 3.696 | 10.000 | 4.000 | 58.000 | 1.12 |
SOE | 43439 | 0.34 | 0.474 | 0.000 | 0.000 | 1.000 | 1.44 |
Indsh | 43439 | 38.913 | 25.54 | 38.913 | 0.000 | 144.675 | 1.31 |
Dual | 43439 | 0.286 | 0.452 | 0.000 | 0.000 | 1.000 | 1.00 |
Variant | Non-Corporate Fraud Companies | Corporate Fraud Companies | Test of Difference |
---|---|---|---|
N = 27,652 | N = 15,787 | ||
Average Value | Average Value | ||
Centrality | 54.037 | 52.289 | 4.25 *** |
Lev | 0.435 | 0.490 | −4.35 *** |
Tobin’s Q | 2.646 | 2.120 | 0.60 |
Top1 | 35.446 | 31.910 | 23.45 *** |
ROE | 0.099 | −0.087 | 4.20 *** |
Size | 22.129 | 21.991 | 10.15 *** |
Age | 13.639 | 14.440 | −9.45 *** |
Board | 9.920 | 11.369 | −40.05 *** |
SOE | 0.369 | 0.289 | 17.15 *** |
Indsh | 40.252 | 36.565 | 14.50 *** |
Dual | 0.290 | 0.279 | 2.50 ** |
Variable | (1) | (2) |
---|---|---|
Centrality | −0.030 *** | −0.056 *** |
(−3.23) | (−6.41) | |
Lev | 0.113 *** | |
(3.85) | ||
Tobin’s Q | −0.001 *** | |
(−3.22) | ||
Top 1 | −0.008 *** | |
(−9.58) | ||
ROE | −0.031 *** | |
(−3.06) | ||
Size | −0.000 *** | |
(−6.71) | ||
Age | 0.017 *** | |
(10.21) | ||
Boardsize | 0.119 *** | |
(35.47) | ||
SOE | −0.482 *** | |
(−17.08) | ||
Indsh | −0.004 *** | |
(−6.92) | ||
Dual | −0.031 | |
(−1.28) | ||
Year | Control | Control |
Industry | Control | Control |
Constant | −1.081 | −0.918 |
(−6.66) | (−5.72) | |
R | 0.067 | 0.028 |
Observations | 43439 | 43439 |
Variable | (1) | (2) | (3) | (4) |
---|---|---|---|---|
DBI | Fraud | Big4 | Fraud | |
Big4 | −0.239 *** | |||
(−4.20) | ||||
DBI | −0.128 *** | |||
(−4.2) | ||||
Centrality | 0.174 *** | −0.041 *** | 0.074 *** | −0.055 *** |
(3.17) | (−6.36) | (3.17) | (−6.36) | |
Lev | −3.386 | 0.631 *** | 0.030 | 0.113 *** |
(0.85) | (3.85) | (0.85) | (3.85) | |
Tobin’s Q | −0.154 *** | 0.022 *** | −0.088 *** | −0.001 *** |
(−4.27) | (−3.22) | (−4.27) | (−3.22) | |
Top 1 | 0.018 * | −0.006 *** | −0.003 * | −0.008 *** |
(−1.69) | (−9.59) | (−1.69) | (−9.59) | |
ROE | 1.308 | −0.796 *** | 0.004 | −0.031 *** |
(0.48) | (−3.06) | (0.48) | (−3.06) | |
Size | 0.000 *** | −0.000 *** | 0.000 *** | −0.000 *** |
(18.64) | (−5.83) | (18.64) | (−5.83) | |
Age | −0.081 | 0.008 *** | −0.002 | 0.017 *** |
(−0.51) | (10.11) | (−0.51) | (10.11) | |
Boardsize | −0.124 | 0.110 *** | 0.001 | 0.119 *** |
(0.10) | (35.47) | (0.10) | (35.47) | |
SOE | 0.501 *** | −0.485 *** | 0.190 *** | −0.481 *** |
(3.08) | (−17.04) | (3.08) | (−17.04) | |
Indsh | 0.000 *** | −0.003 *** | 0.033 *** | −0.003 *** |
(25.24) | (−6.40) | (25.24) | (−6.40) | |
Dual | −0.003 | −0.032 | 0.071 | −0.031 |
(1.26) | (−1.27) | (1.26) | (−1.27) | |
Year | Control | Control | Control | Control |
Industry | Control | Control | Control | Control |
Constant | 8.083 | −0.428 | −4.183 | −1.077 |
(20.78) | (−2.43) | (−9.70) | (−6.64) | |
R | 0.194 | 0.067 | 0.194 | 0.067 |
Observations | 43,439 | 43,439 | 43,439 | 43,439 |
Variable | Fraud | Fraud | Fraud | Fraud | Fraud | ||
---|---|---|---|---|---|---|---|
Heckman | Instrumental Variable | Lag One Phase | Fixed Effect | Small Company | |||
(1) | (2) | (3) | (4) | (5) | (6) | ||
Centrality | −0.056 *** | −1.394 *** | −0.024 *** | −0.058 *** | −0.012 *** | −0.026 * | |
(−6.41) | (−17.56) | (−3.48) | (−6.26) | (−6.55) | (−1.65) | ||
Lev | 0.009 * | 0.108 *** | −0.010 | 0.005 ** | 0.379 *** | 0.005 ** | 0.009 |
(1.77) | (3.66) | (−1.33) | (2.28) | (8.11) | (2.42) | (0.57) | |
Tobin’s Q | 0.005 | −0.004 *** | 0.000 | −0.000 | −0.002 | −0.000 * | −0.000 |
(1.50) | (−6.83) | (0.48) | (−1.60) | (−1.29) | (−1.67) | (−0.66) | |
Top 1 | 0.001 | −0.008 *** | −0.002 *** | −0.002 *** | −0.008 *** | −0.002 *** | −0.008 *** |
(1.26) | (−9.90) | (−3.60) | (−9.86) | (−8.66) | (−9.74) | (−4.35) | |
ROE | −0.002 | −0.030 *** | 0.004 * | −0.001 ** | −0.024 ** | −0.001 *** | −0.013 |
(−1.19) | (−2.93) | (1.88) | (−2.57) | (−2.56) | (−2.69) | (−1.62) | |
Size | −0.000 *** | −0.000 *** | 0.000 *** | −0.000 *** | −0.000 *** | −0.000 *** | −0.000 |
(−3.21) | (−5.94) | (2.99) | (−5.48) | (−6.68) | (−5.79) | (−0.22) | |
Age | −0.010 *** | 0.023 *** | 0.015 *** | 0.004 *** | 0.015 *** | 0.004 *** | 0.037 *** |
(−10.16) | (12.93) | (10.91) | (10.71) | (8.72) | (10.73) | (10.23) | |
Boardsize | −0.059 *** | 0.154 *** | 0.110 *** | 0.026 *** | 0.091 *** | 0.025 *** | 0.118 *** |
(−27.56) | (23.03) | (21.55) | (34.14) | (27.81) | (39.29) | (16.16) | |
SOE | −0.215 *** | −0.355 *** | 0.223 *** | −0.099 *** | −0.430 *** | −0.103 *** | −0.388 *** |
(−12.89) | (−10.39) | (7.63) | (−15.96) | (−14.78) | (−17.35) | (−6.00) | |
Indsh | −0.003 *** | −0.002 ** | 0.004 *** | −0.001 *** | −0.004 *** | −0.001 *** | −0.001 |
(−11.34) | (−2.37) | (7.93) | (−6.44) | (−6.76) | (−7.10) | (−1.46) | |
Dual | 0.019 | −0.042 * | −0.022 | −0.008 | −0.009 | −0.007 | −0.118 ** |
(1.36) | (−1.76) | (−1.16) | (−1.61) | (−0.38) | (−1.39) | (−2.44) | |
IMR | 0.587 *** | ||||||
(6.04) | |||||||
Year | Control | Control | Control | Control | Control | Control | Control |
Industry | Control | Control | Control | Control | Control | Control | Control |
Constant | 1.518 | −1.972 | 3.022 | 0.307 | −0.889 | 0.338 | −2.159 |
(15.20) | (−10.72) | (14.25) | (8.02) | (−5.27) | (10.11) | (−6.55) | |
R2 | 0.067 | 0.067 | 0.000 | 0.081 | 0.048 | 0.064 | 0.066 |
Observations | 43,439 | 43,439 | 43,439 | 43,439 | 38,244 | 43,345 | 10,824 |
Variable | Fraud | Fraud Event | Fraud | Fraud | |||
---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | |
Centrality | −0.021 *** | −0.051 *** | −0.053 *** | ||||
(−15.89) | (−4.85) | (−5.56) | |||||
Centrality-b | −0.010 *** | ||||||
(−2.62) | |||||||
Degree | −0.165 *** | ||||||
(−6.23) | |||||||
Closeness | −1.568 *** | ||||||
(−5.24) | |||||||
Betweenness | −119.265 *** | ||||||
(−5.19) | |||||||
Lev | 0.759 *** | 0.188 *** | 0.190 *** | 0.193 *** | 0.008 *** | 0.181 *** | 0.057 ** |
(12.84) | (5.37) | (5.40) | (5.45) | (5.27) | (3.94) | (2.53) | |
Tobin’s Q | 0.033 *** | −0.002 *** | −0.002 *** | −0.002 *** | −0.000 *** | 0.003 | −0.001 ** |
(4.26) | (−4.68) | (−4.69) | (−4.75) | (−2.70) | (0.68) | (−1.99) | |
Top 1 | −0.006 *** | −0.012 *** | −0.012 *** | −0.012 *** | −0.001 *** | −0.009 *** | −0.007 *** |
(−7.42) | (−12.49) | (−12.28) | (−12.19) | (−6.07) | (−8.54) | (−7.29) | |
ROE | −0.992 *** | −0.027 *** | −0.027 *** | −0.027 *** | −0.002 *** | −0.017 * | −0.019 ** |
(−14.95) | (−2.87) | (−2.93) | (−2.95) | (−5.23) | (−1.95) | (−2.10) | |
Size | −0.000 *** | −0.000 *** | −0.000 *** | −0.000 *** | −0.000 *** | −0.000 *** | −0.000 *** |
(−9.22) | (−7.76) | (−7.82) | (−7.73) | (−6.04) | (−5.61) | (−6.18) | |
Age | 0.001 *** | 0.027 *** | 0.026 *** | 0.026 *** | 0.004 *** | 0.015 *** | 0.015 *** |
(6.11) | (13.75) | (13.39) | (13.35) | (15.09) | (6.92) | (7.90) | |
Boardsize | 0.113 *** | 0.173 *** | 0.168 *** | 0.167 *** | 0.036 *** | 0.121 *** | 0.125 *** |
(32.77) | (44.90) | (45.47) | (45.37) | (79.48) | (27.88) | (33.81) | |
SOE | −0.521 *** | −0.719 *** | −0.722 *** | −0.727 *** | −0.104 *** | −0.427 *** | −0.487 *** |
(−18.32) | (−21.55) | (−21.67) | (−21.85) | (−24.30) | (−11.79) | (−15.79) | |
Indsh | −0.003 *** | −0.005 *** | −0.005 *** | −0.005 *** | −0.001 *** | −0.003 *** | −0.003 *** |
(−6.37) | (−8.27) | (−8.15) | (−8.04) | (−7.96) | (−4.32) | (−5.99) | |
Dual | −0.032 ** | −0.023 | −0.022 | −0.022 | −0.003 | −0.028 | −0.031 |
(−1.33) | (−0.81) | (−0.79) | (−0.79) | (−0.07) | (−0.96) | (−1.15) | |
Year | Control | Control | Control | Control | Control | Control | Control |
Industry | Control | Control | Control | Control | Control | Control | Control |
Constant | −1.421 | −2.014 | −2.238 | −2.287 | −0.127 | −1.462 | −1.067 |
(−8.60) | (−10.74) | (−12.32) | (−12.62) | (−5.01) | (−11.14) | (−6.07) | |
R2 | 0.073 | 0.155 | 0.155 | 0.155 | 0.179 | 0.061 | 0.058 |
Observations | 43,338 | 43,439 | 43,439 | 43,439 | 43,345 | 28,297 | 34,004 |
Variable | Fraud | Fraud | Fraud |
---|---|---|---|
Non-Independent Directors Network (1) | Independent Directors Network (2) | Women Directors Network (3) | |
Centrality | −1.161 *** | −0.955 *** | −0.049 *** |
(−21.05) | (−19.01) | (−2.60) | |
Lev | 0.202 *** | 0.231 *** | 0.191 *** |
(5.57) | (6.10) | (5.40) | |
Tobin’s Q | −0.002 *** | −0.002 *** | −0.002 *** |
(−4.86) | (−5.41) | (−4.70) | |
Top 1 | −0.013 *** | −0.014 *** | −0.012 *** |
(−13.08) | (−14.20) | (−12.11) | |
ROE | −0.030 *** | −0.030 *** | −0.027 *** |
(−3.15) | (−3.15) | (−2.95) | |
Size | −0.000 *** | −0.000 *** | −0.000 *** |
(−6.20) | (−6.57) | (−8.02) | |
Age | 0.033 *** | 0.025 *** | 0.025 *** |
(16.43) | (13.45) | (13.06) | |
Boardsize | 0.209 *** | 0.202 *** | 0.167 *** |
(46.09) | (46.47) | (45.32) | |
SOE | −0.608 *** | −0.598 *** | −0.729 *** |
(−17.83) | (−17.90) | (−21.88) | |
Indsh | −0.004 *** | −0.004 *** | −0.005 *** |
(−5.80) | (−6.04) | (−8.16) | |
Dual | −0.026 | −0.023 | −0.020 |
(−0.94) | (−0.82) | (−0.71) | |
Year | Control | Control | Control |
Industry | Control | Control | Control |
Constant | 0.627 | 0.248 | −2.329 |
(2.73) | (1.11) | (−12.87) | |
R | 0.116 | 0.115 | 0.115 |
Observations | 42,630 | 42,627 | 43,312 |
Variable | Fraud | Fraud | Fraud |
---|---|---|---|
(1) | (2) | (3) | |
Centrality | −0.019 ** | −0.003 | −0.070 *** |
(−2.21) | (−0.29) | (−3.90) | |
Lev | 0.114 *** | 0.114 *** | 0.113 *** |
(3.86) | (3.86) | (3.85) | |
Tobin’s Q | −0.001 *** | −0.001 *** | −0.001 *** |
(−3.25) | (−3.25) | (−3.23) | |
Top 1 | −0.008 *** | −0.008 *** | −0.008 *** |
(−9.57) | (−9.55) | (−9.63) | |
ROE | −0.031 *** | −0.032 *** | −0.032 *** |
(−3.09) | (−3.10) | (−3.10) | |
Size | −0.000 *** | −0.000 *** | −0.000 *** |
(−6.85) | (−6.90) | (−6.76) | |
Age | 0.016 *** | 0.016 *** | 0.016 *** |
(10.01) | (9.95) | (9.99) | |
Boardsize | 0.117 *** | 0.116 *** | 0.117 *** |
(34.46) | (34.05) | (35.23) | |
SOE | −0.493 *** | −0.494 *** | −0.491 *** |
(−17.53) | (−17.57) | (−17.42) | |
Indsh | −0.004 *** | −0.004 *** | −0.004 *** |
(−7.14) | (−7.25) | (−6.94) | |
Dual | −0.030 | −0.030 | −0.030 |
(−1.26) | (−1.25) | (−1.26) | |
Year | Control | Control | Control |
Industry | Control | Control | Control |
Constant | −1.201 | −1.196 | −1.196 |
(−7.44) | (−7.41) | (−7.41) | |
R | 0.066 | 0.066 | 0.066 |
Observations | 43,338 | 43,338 | 43,338 |
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Zeng, S.; Xiao, L.; Jiang, X.; Huang, Y.; Li, Y.; Yuan, C. Do Directors’ Network Positions Affect Corporate Fraud? Sustainability 2024 , 16 , 6675. https://doi.org/10.3390/su16156675
Zeng S, Xiao L, Jiang X, Huang Y, Li Y, Yuan C. Do Directors’ Network Positions Affect Corporate Fraud? Sustainability . 2024; 16(15):6675. https://doi.org/10.3390/su16156675
Zeng, Sen, Longjun Xiao, Xueyan Jiang, Yiqian Huang, Yanru Li, and Cao Yuan. 2024. "Do Directors’ Network Positions Affect Corporate Fraud?" Sustainability 16, no. 15: 6675. https://doi.org/10.3390/su16156675
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Hypothesis testing is a common statistical tool used in research and data science to support the certainty of findings. The aim of testing is to answer how probable an apparent effect is detected by chance given a random data sample. This article provides a detailed explanation of the key concepts in Frequentist hypothesis testing using problems from the business domain as examples.
In binary hypothesis testing problems, we'll often be presented with two choices which we call hypotheses, and we'll have to decide whether to pick one or the other. The hypotheses are represented by H₀ and H₁ and are called null and alternate hypotheses respectively. In hypothesis testing, we either reject or accept the null hypothesis.
Hypothesis testing is a method of statistical inference that considers the null hypothesis H ₀ vs. the alternative hypothesis H a, where we are typically looking to assess evidence against H ₀. Such a test is used to compare data sets against one another, or compare a data set against some external standard. The former being a two sample ...
Learn how to perform hypothesis testing for data science projects with this easy guide. Find examples, tips, and related articles on statistics and machine learning.
Hypothesis Testing in Data Science is a crucial method for making informed decisions from data. This blog explores its essential usage in analysing trends and patterns, and the different types such as null, alternative, one-tailed, and two-tailed tests, providing a comprehensive understanding for both beginners and advanced practitioners.
1. Introduction to Hypothesis Testing - Definition and significance in research and data analysis. - Brief historical background. 2. Fundamentals of Hypothesis Testing - Null and Alternative…
Data Science from Scratch (ch7) - Hypothesis and Inference. Connecting probability and statistics to hypothesis testing and inference. This is a continuation of my progress through Data Science from Scratch by Joel Grus. We'll use a classic coin-flipping example in this post because it is simple to illustrate with both .
Statistical analysis forms the backbone of any data science workflow. Among the statistical concepts we regularly encounter in data science, Hypothesis Testing is one of the most essential.
Explore what is hpothesis testing is in data science? Read on more to understand the types of hypothesis testing, importance, workflow & real world examples of statistical hypothesis.
Hypothesis testing is a quintessential part of statistical inference in data science context.
This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data.
Hypothesis testing can be thought of as a way to investigate the consistency of a dataset with a model, where a model is a set of rules that describe how data are generated. The consistency is evaluated using ideas from probability and probability distributions.
Hypothesis testing is a technique that helps scientists, researchers, or for that matter, anyone test the validity of their claims or hypotheses about real-world or real-life events in order to establish new knowledge. Hypothesis testing techniques are often used in statistics and data science to analyze whether the claims about the occurrence of the events are true, whether the results ...
Hypothesis Testing vs Hypothesis Generation In the world of Data Science, there are two parts to consider when putting together a hypothesis. Hypothesis Testing is when the team builds a strong hypothesis based on the available dataset. This will help direct the team and plan accordingly throughout the data science project.
Quick-reference guide to the 17 statistical hypothesis tests that you need in applied machine learning, with sample code in Python. Although there are hundreds of statistical hypothesis tests that you could use, there is only a small subset that you may need to use in a machine learning project. In this post, you will discover […]
Hypothesis testing is an important mathematical concept that's used in the field of data science. While it's really easy to call a random method from a python library that'll carry out the test for you, it's both necessary and interesting to know what is actually happening behind the scenes!
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.
Hypothesis testing is a statistical method that is used to make a statistical decision using experimental data. Hypothesis testing is basically an assumption that we make about a population parameter. It evaluates two mutually exclusive statements about a population to determine which statement is best supported by the sample data.
Hypothesis tests # Formal hypothesis testing is perhaps the most prominent and widely-employed form of statistical analysis. It is sometimes seen as the most rigorous and definitive part of a statistical analysis, but it is also the source of many statistical controversies. The currently-prevalent approach to hypothesis testing dates to developments that took place between 1925 and 1940 ...
During an interview, emphasizing your proficiency in hypothesis testing demonstrates your ability to approach problems scientifically and draw meaningful conclusions from data. When discussing hypothesis testing, focus on your ability to formulate clear null and alternative hypotheses, select appropriate significance levels, and interpret p-values.
Hypothesis testing is a statistical method used to determine if there is enough evidence in a sample data to draw conclusions about a population. It involves formulating two competing hypotheses, the null hypothesis (H0) and the alternative hypothesis (Ha), and then collecting data to assess the evidence.
Learn how to perform a z-test in statistics and understand its significance in hypothesis testing for data science and data analysis. This tutorial will guid...
The network data has attracted considerable attention in modern statistics. In research on complex network data, one key issue is finding its underlying connection structure given a network sample. The methods that have been proposed in literature usually assume that the underlying structure is a known model. In practice, however, the true model is usually unknown, and network learning ...
Understanding the intuition behind Hypothesis Testing. What exactly it is, why do we do it and how do Data Scientists perform it. Let's…
Multi-level, hybrid models and simulations are essential to enable predictions and hypothesis generation in systems biology research. However, the computational complexity of these models poses a bottleneck, limiting the applicability of methodologies relying on large number of simulations, such as the Optimization via Simulation (OvS) of complex biological processes.
Corporate fraud poses a significant obstacle for sustainable business development. Drawing on social network analysis, this paper used data originated from Chinese-listed companies from 2009 to 2022 and found that directors' network position significantly mitigates corporate fraud. Mechanism tests indicated that the quality of external auditors and internal control play a mediating role in ...
I spent time scouring the Internet for resources to better understand concepts like hypothesis testing and confidence intervals. And after interviewing for multiple data science positions, I've found that most statistics interview questions followed a similar pattern.
Semantic Scholar extracted view of "Testing the religion/spirituality-mental health curvilinear hypothesis using data from many-analysts religion project" by Luke Galen et al.
In this article, I want to show hypothesis testing with Python on several questions step-by-step. But before, let me explain the hypothesis testing process briefly. If you wish, you can move to the questions directly.
Here, we provide evidence for such a mosaic hypothesis by developing a statistical point process analysis framework for spatial transcriptomic data. We demonstrate spatial avoidance across many excitatory and inhibitory neuronal types.